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# AI Agent Instructions for GLAM Data Extraction
This document provides instructions for AI agents (particularly OpenCODE and Claude) to assist with extracting heritage institution data from conversation JSON files and other sources.
---
## 🎯 PROJECT CORE MISSION
**PRIMARY OBJECTIVE**: Create a comprehensive, nuanced ontology that accurately represents the complex, temporal, multi-faceted nature of heritage custodian institutions worldwide.
This is NOT a simple data extraction project. This is an **ontology engineering project** that:
- Models heritage entities as multi-aspect temporal entities (place, custodian, legal form, collections, people)
- Integrates multiple base ontologies (CPOV, TOOI, CIDOC-CRM, RiC-O, Schema.org, PiCo)
- Captures organizational change events over time (custody transfers, mergers, transformations)
- Distinguishes between nominal references and formal organizational structures
- Links heritage custodians to people, collections, and locations with independent temporal lifecycles
**If you're looking for simple NER extraction, this is not the right project.**
---
## 🚨 CRITICAL RULES FOR ALL AGENTS
### Rule 0: LinkML Schemas Are the Single Source of Truth
**MASTER SCHEMA LOCATION**: `schemas/20251121/linkml/`
The LinkML schema files are the **authoritative, canonical definition** of the Heritage Custodian Ontology:
**Primary Schema File** (SINGLE SOURCE OF TRUTH):
- `schemas/20251121/linkml/01_custodian_name.yaml` - Complete Heritage Custodian Ontology
- Defines CustodianObservation (source-based references to heritage keepers)
- Defines CustodianName (standardized emic names)
- Defines CustodianReconstruction (formal entities: individuals, groups, organizations, governments, corporations)
- Includes ISO 20275 legal form codes (for legal entities)
- PiCo-inspired observation/reconstruction pattern
- Based on CIDOC-CRM E39_Actor (broader than organization)
**ALL OTHER FILES ARE DERIVED/GENERATED** from these LinkML schemas:
❌ **DO NOT** edit these derived files directly:
- `schemas/20251121/rdf/*.{ttl,nt,jsonld,rdf,n3,trig,trix}` - **GENERATED** from LinkML via `gen-owl` + `rdfpipe`
- `schemas/20251121/typedb/*.tql` - **DERIVED** TypeDB schema (manual translation from LinkML)
- `schemas/20251121/uml/mermaid/*.mmd` - **DERIVED** UML diagrams (manual visualization of LinkML)
- `schemas/20251121/examples/*.yaml` - **INSTANCES** conforming to LinkML schema
**Workflow for Schema Changes**:
```
1. EDIT LinkML schema (01_custodian_name.yaml)
↓
2. REGENERATE RDF formats (WITH FULL TIMESTAMPS):
$ TIMESTAMP=$(date +%Y%m%d_%H%M%S)
$ gen-owl -f ttl schemas/20251121/linkml/01_custodian_name.yaml 2>/dev/null > schemas/20251121/rdf/01_custodian_name_${TIMESTAMP}.owl.ttl
$ rdfpipe schemas/20251121/rdf/01_custodian_name_${TIMESTAMP}.owl.ttl -o nt > schemas/20251121/rdf/01_custodian_name_${TIMESTAMP}.nt
$ rdfpipe schemas/20251121/rdf/01_custodian_name_${TIMESTAMP}.owl.ttl -o jsonld > schemas/20251121/rdf/01_custodian_name_${TIMESTAMP}.jsonld
$ # ... repeat for all 8 formats (ALL with same timestamp)
↓
3. REGENERATE UML diagrams (WITH FULL TIMESTAMPS):
$ gen-yuml schemas/20251121/linkml/01_custodian_name.yaml > schemas/20251121/uml/mermaid/01_custodian_name_${TIMESTAMP}.mmd
↓
4. UPDATE TypeDB schema (manual translation)
↓
5. VALIDATE example instances:
$ linkml-validate -s schemas/20251121/linkml/01_custodian_name.yaml schemas/20251121/examples/example.yaml
```
**🚨 CRITICAL RULE: Full Timestamps Required**
ALL generated files MUST include full timestamps (date AND time) in filenames:
**Format**: `{base_name}_{YYYYMMDD}_{HHMMSS}.{extension}`
**Examples**:
```bash
# ✅ CORRECT - Full timestamp (date + time)
custodian_multi_aspect_20251122_154430.owl.ttl
custodian_multi_aspect_20251122_154430.nt
custodian_multi_aspect_20251122_154430.jsonld
custodian_multi_aspect_20251122_154136.mmd
# ❌ WRONG - Date only (MISSING TIME!)
custodian_multi_aspect_20251122.owl.ttl
# ❌ WRONG - No timestamp
01_custodian_name.owl.ttl
```
**Rationale**: Full timestamps (date + time) allow multiple generation runs per day without filename conflicts, enable precise version tracking, and provide clear audit trails for schema evolution.
**See**: `.opencode/SCHEMA_GENERATION_RULES.md` for complete generation rules
**Why LinkML is the Master**:
- ✅ **Formal specification**: Type-safe, validation rules, cardinality constraints
- ✅ **Multi-format generation**: Single source → RDF, JSON-LD, Python, SQL, GraphQL
- ✅ **Version control**: Clear diffs, semantic versioning, change tracking
- ✅ **Ontology alignment**: Explicit `class_uri` and `slot_uri` mappings to base ontologies
- ✅ **Documentation**: Rich inline documentation with examples
**NEVER**:
- ❌ Edit RDF files directly (they will be overwritten on next generation)
- ❌ Consider TypeDB schema as authoritative (it's a translation target)
- ❌ Treat UML diagrams as specification (they're visualizations)
**ALWAYS**:
- ✅ Refer to LinkML schemas for class definitions
- ✅ Update LinkML first, then regenerate derived formats
- ✅ Validate changes against LinkML metamodel
- ✅ Document schema changes in LinkML YAML comments
**See also**:
- `schemas/20251121/RDF_GENERATION_SUMMARY.md` - RDF generation process documentation
- `docs/MIGRATION_GUIDE.md` - Schema migration procedures
- LinkML documentation: https://linkml.io/
---
### Rule 1: Ontology Files Are Your Primary Reference
**BEFORE** designing any schema, class, or property:
1. **READ the base ontology files** in `/data/ontology/`
2. **SEARCH for existing classes and properties** that match your needs
3. **DOCUMENT your ontology alignment** with explicit rationale
4. **NEVER invent custom properties** when ontology equivalents exist
**Available Ontologies**:
- `data/ontology/core-public-organisation-ap.ttl` - CPOV (EU public sector)
- `data/ontology/tooiont.ttl` - TOOI (Dutch government)
- `data/ontology/schemaorg.owl` - Schema.org (web semantics, private sector)
- `data/ontology/CIDOC_CRM_v7.1.3.rdf` - CIDOC-CRM (cultural heritage domain)
- `data/ontology/RiC-O_1-1.rdf` - Records in Contexts (archival description)
- `data/ontology/bibframe_vocabulary.rdf` - BIBFRAME (libraries)
- `data/ontology/pico.ttl` - PiCo (person observations, staff roles)
**See** `.opencode/agent/ontology-mapping-rules.md` for complete ontology consultation workflow.
### Rule 2: Wikidata Entities Are NOT Ontology Classes
**Files**:
- `data/wikidata/GLAMORCUBEPSXHFN/hyponyms_curated.yaml`
- `data/wikidata/GLAMORCUBEPSXHFN/hyponyms_curated_full.yaml`
**These files contain**:
- ✅ Wikidata entity identifiers (Q-numbers) for heritage institution TYPES
- ✅ Multilingual labels and descriptions
- ✅ Hypernym classifications (upper-level categories)
- ✅ Source data for ontology mapping analysis
**These files DO NOT contain**:
- ❌ Formal ontology class definitions
- ❌ Direct `class_uri` mappings for LinkML
- ❌ Ontology properties or relationships
**REQUIRED WORKFLOW**:
```
hyponyms_curated.yaml (Wikidata Q-numbers)
↓
ANALYZE semantic meaning + hypernyms
↓
SEARCH base ontologies for matching classes
↓
MAP Wikidata entity → Ontology class(es)
↓
DOCUMENT rationale + properties
↓
CREATE LinkML schema with ontology class_uri
```
**Example - WRONG** ❌:
```yaml
Mansion:
class_uri: wd:Q1802963 # ← This is an ENTITY, not a CLASS!
```
**Example - CORRECT** ✅:
```yaml
Mansion:
# Wikidata source: Q1802963
place_aspect:
class_uri: crm:E27_Site # CIDOC-CRM ontology class
custodian_aspect:
class_uri: cpov:PublicOrganisation # If operates as museum
```
### Rule 3: Multi-Aspect Modeling is Mandatory
**Every heritage entity has MULTIPLE ontological aspects with INDEPENDENT temporal lifecycles.**
**Required Aspects**:
1. **Place Aspect** (physical location/site)
- Ontology: `crm:E27_Site` + `schema:Place`
- Temporal: Construction → Demolition/Present
2. **Custodian Aspect** (organization managing heritage)
- Ontology: `cpov:PublicOrganisation` OR `schema:Organization`
- Temporal: Founding → Dissolution/Present
3. **Legal Form Aspect** (legal entity registration)
- Ontology: `org:FormalOrganization` + `tooi:Overheidsorganisatie` (Dutch)
- Temporal: Registration → Deregistration/Present
4. **Collections Aspect** (heritage materials)
- Ontology: `rico:RecordSet` OR `crm:E78_Curated_Holding` OR `bf:Collection`
- Temporal: Accession → Deaccession (per item)
5. **People Aspect** (staff, curators)
- Ontology: `pico:PersonObservation` + `crm:E21_Person`
- Temporal: Employment start → Employment end (per person)
6. **Temporal Events** (organizational changes)
- Ontology: `crm:E10_Transfer_of_Custody`, `rico:Event`
- Tracks custody transfers, mergers, relocations, transformations
**Example**: A historic mansion operating as a museum has:
- **Place aspect**: Building constructed 1880, still standing (143 years)
- **Custodian aspect**: Foundation established 1994 to operate museum (30 years)
- **Legal form**: Dutch stichting registered 1994, KvK #12345678
- **Collections**: Mondrian artworks acquired 1994-2024
- **People**: Current curator employed 2020-present
**Each aspect changes independently over time!**
### Rule 5: NEVER Delete Enriched Data - Additive Only
**🚨 CRITICAL: Data enrichment is ADDITIVE ONLY. Never delete or overwrite existing enriched content.**
When restructuring or updating enriched institution records:
**✅ ALLOWED (Additive Operations)**:
- Add new fields or sections
- Restructure YAML/JSON layout while preserving all content
- Rename files (e.g., `_unknown.yaml` → `_museum_name.yaml`)
- Add provenance metadata
- Merge data from multiple sources (preserving all)
**❌ FORBIDDEN (Destructive Operations)**:
- Delete Google Maps data (reviews, ratings, photo counts, popular times)
- Remove OpenStreetMap metadata
- Overwrite website scrape results
- Delete Wikidata enrichment data
- Remove any `*_enrichment` sections
- Truncate or summarize detailed content
**Data Types That Must NEVER Be Deleted**:
| Data Source | Protected Fields |
|-------------|------------------|
| **Google Maps** | `reviews`, `rating`, `total_ratings`, `photo_count`, `popular_times`, `place_id`, `business_status` |
| **OpenStreetMap** | `osm_id`, `osm_type`, `osm_tags`, `amenity`, `building`, `heritage` |
| **Wikidata** | `wikidata_id`, `claims`, `sitelinks`, `aliases`, `descriptions` |
| **Website Scrape** | `organization_details`, `collections`, `exhibitions`, `contact`, `social_media`, `accessibility` |
| **ISIL Registry** | `isil_code`, `assigned_date`, `remarks` |
**Example - CORRECT Restructuring**:
```yaml
# BEFORE (flat structure)
google_maps_rating: 4.5
google_maps_reviews: 127
website_description: "Historic museum..."
# AFTER (nested structure) - ALL DATA PRESERVED
enrichment_sources:
google_maps:
rating: 4.5 # ← PRESERVED
reviews: 127 # ← PRESERVED
website:
description: "Historic museum..." # ← PRESERVED
```
**Example - WRONG (Data Loss)**:
```yaml
# BEFORE
google_maps_enrichment:
rating: 4.5
reviews: 127
popular_times: {...}
photos: [...]
# AFTER - WRONG! Data deleted!
enrichment_status: enriched
# Where did the rating, reviews, popular_times go?!
```
**Rationale**:
- Enriched data is expensive to collect (API calls, rate limits, web scraping)
- Google Maps data changes over time - historical snapshots are valuable
- Reviews and ratings provide quality signals for heritage institutions
- Photo metadata enables visual discovery and verification
- Deleting data violates data provenance principles
**If Unsure**: When restructuring files, first READ the entire file, then WRITE a new version that includes ALL original content in the new structure.
---
### Rule 6: WebObservation Claims MUST Have XPath Provenance
**Every claim extracted from a webpage MUST have an XPath pointer to the exact location in archived HTML where that value appears. Claims without XPath provenance are FABRICATED and must be removed.**
This is not about "confidence" or "uncertainty" - it's about **verifiability**. Either the claim value exists in the HTML at a specific XPath, or it was hallucinated/fabricated by an LLM.
**Required Fields for WebObservation Claims**:
| Field | Required | Description |
|-------|----------|-------------|
| `claim_type` | YES | Type of claim (full_name, description, email, etc.) |
| `claim_value` | YES | The extracted value |
| `source_url` | YES | URL the claim was extracted from |
| `retrieved_on` | YES | ISO 8601 timestamp when page was archived |
| `xpath` | YES | XPath to the element containing this value |
| `html_file` | YES | Relative path to archived HTML file |
| `xpath_match_score` | YES | 1.0 for exact match, <1.0 for fuzzy match |
**Example - CORRECT (Verifiable)**:
```yaml
web_enrichment:
claims:
- claim_type: full_name
claim_value: Historische Vereniging Nijeveen
source_url: https://historischeverenigingnijeveen.nl/
retrieved_on: "2025-11-29T12:28:00Z"
xpath: /[document][1]/html[1]/body[1]/div[6]/div[1]/table[3]/tbody[1]/tr[1]/td[1]/p[6]
html_file: web/0021/historischeverenigingnijeveen.nl/rendered.html
xpath_match_score: 1.0
```
**Example - WRONG (Fabricated - Must Be Removed)**:
```yaml
web_enrichment:
claims:
- claim_type: full_name
claim_value: Historische Vereniging Nijeveen
confidence: 0.95 # ← NO! This is meaningless without XPath
```
**Workflow**:
1. Archive website using Playwright: `python scripts/fetch_website_playwright.py <entry> <url>`
2. Add XPath provenance: `python scripts/add_xpath_provenance.py`
3. Script removes fabricated claims (stored in `removed_unverified_claims` for audit)
**See**:
- `.opencode/WEB_OBSERVATION_PROVENANCE_RULES.md` for complete documentation
- `schemas/20251121/linkml/modules/classes/WebClaim.yaml` for LinkML schema definition
---
### Rule 4: Technical Classes Are Excluded from Visualizations
**Some LinkML classes exist solely for validation purposes and have NO semantic significance.**
These "scaffolding" classes are essential for building (validation, parsing) but are not part of the final ontology structure. They MUST be excluded from UML diagrams, entity-relationship diagrams, and other semantic visualizations.
**Currently Excluded Classes**:
| Class | Purpose | Why Excluded |
|-------|---------|--------------|
| `Container` | LinkML `tree_root` for instance validation | No `class_uri`, not serialized to RDF, purely structural |
**The `Container` Class**:
**Definition**: `schemas/20251121/linkml/modules/classes/Container.yaml`
```yaml
Container:
description: >-
Root container for validating complete instance documents.
Not used in RDF serialization (flattened).
tree_root: true # ← This is why it exists
# Notice: NO class_uri - not mapped to any ontology
```
**Purpose**:
- Enables `linkml-validate -C Container instance.yaml` validation
- Provides entry point for parsing YAML/JSON instance documents
- Has NO semantic meaning in the domain model
**Exclusion Implementation**:
- Python generators: `EXCLUDED_CLASSES = {"Container"}` in `scripts/generate_*.py`
- Frontend parsers: `EXCLUDED_CLASSES` Set in `frontend/src/components/uml/UMLParser.ts`
**Verification**:
```bash
# Check that Container is NOT in diagrams
grep "Container" schemas/20251121/uml/mermaid/complete_schema_*.mmd
# Should return no matches
# Verify class count (should be 71, not 72)
grep -c "class " schemas/20251121/uml/mermaid/complete_schema_*.mmd | tail -1
```
**See**: `.opencode/LINKML_TECHNICAL_CLASSES.md` for complete documentation and how to add new exclusions.
---
### Rule 7: Deployment is LOCAL via SSH/rsync (NO CI/CD)
**🚨 CRITICAL: This project has NO GitHub Actions, NO CI/CD pipelines. ALL deployments are executed LOCALLY via SSH and rsync.**
**Server Information**:
- **Provider**: Hetzner Cloud
- **IP Address**: `91.98.224.44`
- **SSH User**: `root`
- **Frontend Path**: `/var/www/glam-frontend/`
- **Data Path**: `/mnt/data/`
**Deployment Script**: `infrastructure/deploy.sh`
```bash
# Deploy frontend only (most common)
./infrastructure/deploy.sh --frontend
# Deploy data files only
./infrastructure/deploy.sh --data
# Deploy infrastructure configs
./infrastructure/deploy.sh --infra
# Deploy everything
./infrastructure/deploy.sh --all
# Check server status
./infrastructure/deploy.sh --status
# Reload Caddy server
./infrastructure/deploy.sh --reload
```
**Prerequisites**:
1. `.env` file with `HETZNER_HC_API_TOKEN` (for Hetzner Cloud API)
2. SSH access to server (`ssh root@91.98.224.44`)
3. Local dependencies installed (`npm`, `node` for frontend builds)
**Frontend Deployment Process**:
```
1. npm run build (in frontend/)
2. Sync LinkML schemas to frontend/public/schemas/
3. Generate manifest.json for schema versions
4. rsync build output to server /var/www/glam-frontend/
```
**AI Agent Rules**:
✅ **DO**:
- Use `./infrastructure/deploy.sh --frontend` after frontend changes
- Verify deployment with `./infrastructure/deploy.sh --status`
- Build frontend locally before deploying (`cd frontend && npm run build`)
- Check SSH connectivity before attempting deployment
❌ **DON'T**:
- Create GitHub Actions workflows
- Create CI/CD configuration files
- Assume automatic deployment on git push
- Deploy without building first
- Modify server configurations directly (use `--infra` flag)
**Post-Deployment Verification**:
```bash
# Check server is responding
curl -I https://91.98.224.44
# SSH and check files
ssh root@91.98.224.44 "ls -la /var/www/glam-frontend/"
# Check Caddy status
ssh root@91.98.224.44 "systemctl status caddy"
```
**See**:
- `.opencode/DEPLOYMENT_RULES.md` for complete deployment rules
- `docs/DEPLOYMENT_GUIDE.md` for comprehensive deployment documentation
- `infrastructure/deploy.sh` for script source code
---
### Rule 8: Legal Form Terms MUST Be Filtered from CustodianName
**🚨 CRITICAL EXCEPTION TO EMIC PRINCIPLE: Legal form designations are ALWAYS filtered from CustodianName, even when the custodian self-identifies with them.**
This is the **ONE EXCEPTION** to the emic (insider name) principle. Legal forms are metadata about the entity, not part of its identity.
**Why This Rule Exists**:
1. **Legal Form ≠ Identity**: "Stichting Rijksmuseum" has legal form "Stichting" (foundation), but the IDENTITY is "Rijksmuseum"
2. **Legal Forms Change**: Foundations become corporations, associations become foundations - the identity persists
3. **Cross-Jurisdictional Consistency**: "Getty Foundation" (US) and "Stichting Getty" (NL) are the same identity
4. **Deduplication**: Prevents "Museum X" and "Stichting Museum X" appearing as separate entities
5. **ISO 20275 Alignment**: Legal Entity Identifier standard separates legal form from entity name
**Examples**:
| Source Name | CustodianName | Legal Form (metadata) |
|-------------|---------------|----------------------|
| Stichting Rijksmuseum | Rijksmuseum | Stichting |
| Hidde Nijland Stichting | Hidde Nijland | Stichting |
| The Getty Foundation | The Getty | Foundation |
| British Museum Trust Ltd | British Museum | Trust Ltd |
| Fundação Biblioteca Nacional | Biblioteca Nacional | Fundação |
| Verein Deutsches Museum | Deutsches Museum | Verein |
| Association des Amis du Louvre | Amis du Louvre | Association |
**Legal Form Terms to Filter** (partial list by language):
| Language | Terms to Remove |
|----------|-----------------|
| **Dutch** | Stichting, Coöperatie, Maatschap, B.V., N.V., V.O.F., C.V. |
| **English** | Foundation, Trust, Inc., Incorporated, Ltd., Limited, LLC, Corp., Corporation |
| **German** | Stiftung, e.V., GmbH, AG, KG, OHG |
| **French** | Fondation, S.A., S.A.R.L., S.C.I., S.N.C. |
| **Spanish** | Fundación, S.A., S.L., S.L.L. |
| **Portuguese** | Fundação, Ltda., S.A. |
| **Italian** | Fondazione, S.p.A., S.r.l. |
**⚠️ Terms NOT to Filter** (these describe organizational purpose, not legal form):
| Language | Keep These (Part of Identity) |
|----------|-------------------------------|
| **Dutch** | Vereniging, Genootschap, Kring, Bond |
| **English** | Association, Society, Guild, Club |
| **German** | Verein, Gesellschaft, Bund |
| **French** | Association, Société, Cercle |
| **Spanish** | Asociación, Sociedad |
| **Portuguese** | Associação, Sociedade |
| **Italian** | Associazione, Società |
**Rationale**: "Vereniging" (Dutch), "Association" (English/French), "Verein" (German), etc. describe **what the organization IS** (a voluntary association of members), not just how it's legally registered. "Historische Vereniging Nijeveen" is fundamentally different from "Stichting Rijksmuseum" - the former is a membership organization, the latter is a foundation. Filtering these terms destroys meaningful identity information.
**Implementation**:
1. **CustodianName.yaml**: Legal form filtering documented in schema description
2. **Extraction Scripts**: Must strip legal form terms before storing `custodian_name`
3. **GHCID Generation**: Uses filtered name for abbreviation generation
4. **Validation**: Scripts should flag names containing legal form terms
**Where Legal Form IS Stored**:
Legal form is NOT discarded - it is stored in separate metadata fields:
- `CustodianLegalStatus.legal_form` - The ISO 20275 legal form code
- `CustodianLegalStatus.legal_name` - Full registered name including legal form
- `CustodianObservation.observed_name` - Original name as observed in source (may include legal form)
**See**: `.opencode/LEGAL_FORM_FILTERING_RULE.md` for comprehensive global legal form list
---
### Rule 9: Enum-to-Class Promotion - Single Source of Truth
**🚨 CRITICAL: When an enum is promoted to a class hierarchy, the original enum MUST be deleted. Never maintain parallel enum/class definitions.**
This project follows the **Single Source of Truth** principle for type systems. An enum and a class hierarchy representing the same concept creates dangerous duplication that leads to:
- Inconsistent values between enum and class definitions
- Confusion about which to use in slot ranges
- Maintenance burden of keeping both in sync
- Validation failures from mismatched values
**Lifecycle of Enum-to-Class Promotion**:
```
1. ENUM (Scaffolding Phase)
- Simple list of values
- No properties needed yet
- Used in slot range: MyEnum
↓
2. DECISION: Needs properties/hierarchy?
- Does concept need properties (description, category, related_to)?
- Does concept need inheritance (subtypes)?
- Does concept need rich documentation per value?
↓
3. CLASS HIERARCHY (Production Phase)
- Abstract base class with shared properties
- Concrete subclasses for each value
- Used in slot range: MyClass (polymorphic)
↓
4. DELETE ENUM ← MANDATORY!
- Archive enum file: modules/enums/archive/MyEnum.yaml.archived_YYYYMMDD
- Remove from manifest.json
- Remove from instance files
- Update all slot ranges to use class
```
**Enums That Remain Permanent** (based on external standards, no properties needed):
- `DataTierEnum` - Fixed 4-tier quality model
- `CountryCodeEnum` - ISO 3166-1 alpha-2 codes
- `LanguageCodeEnum` - ISO 639-1 codes
- Simple status enums with no semantic richness
**Example: StaffRoleTypeEnum → StaffRole Class Hierarchy**
```yaml
# BEFORE (Enum phase - scaffolding)
StaffRoleTypeEnum:
permissible_values:
CURATOR: {}
ARCHIVIST: {}
CONSERVATOR: {}
staff_role:
range: StaffRoleTypeEnum # Simple enum reference
# AFTER (Class hierarchy - production)
StaffRole:
abstract: true
slots:
- role_category # RoleCategoryEnum
- typical_domains # List of CustodianType
- pico_alignment # PiCo ontology class_uri
Curator:
is_a: StaffRole
slot_usage:
role_category: { equals_string: CURATORIAL }
staff_role:
range: StaffRole # Polymorphic class reference
# StaffRoleTypeEnum.yaml → ARCHIVED (deleted from active schema)
```
**When You Encounter This Situation**:
1. **Check for existing class hierarchy**: Search `modules/classes/` for equivalent class
2. **If class exists**: Use class in slot range, ensure enum is archived
3. **If promoting enum**: Create class hierarchy, update slot ranges, archive enum
4. **Document migration**: Add comment in archived file explaining reason
**Archive Location**: `schemas/20251121/linkml/archive/enums/`
**See**:
- `.opencode/ENUM_TO_CLASS_PRINCIPLE.md` for complete documentation
- `docs/ENUM_CLASS_SINGLE_SOURCE.md` for case study (StaffRoleTypeEnum migration)
---
### Rule 10: CH-Annotator is the Entity Annotation Convention
**🚨 CRITICAL: All entity annotation in this project MUST follow the CH-Annotator convention (ch_annotator-v1_7_0).**
**Convention Identity**:
- **ID**: `ch_annotator-v1_7_0`
- **Full Name**: CH-Annotator (Cultural Heritage Annotator)
- **Version**: 1.7.0
- **Status**: PRODUCTION
- **Formerly Known As**: GLAM-NER v1.7.0-unified (renamed 2025-12-06)
**Primary File Location**:
```
data/entity_annotation/ch_annotator-v1_7_0.yaml
```
**What CH-Annotator Covers**:
- Named Entity Recognition (NER)
- Property Extraction
- Entity Resolution
- Entity Linking
- Claim Validation
- Document Structure Annotation
**Hypernym Entity Types** (9 categories):
| Code | Hypernym | Primary Ontology |
|------|----------|------------------|
| **AGT** | AGENT | `crm:E39_Actor` |
| **GRP** | GROUP | `crm:E74_Group` |
| **TOP** | TOPONYM | `crm:E53_Place` |
| **GEO** | GEOMETRY | `geo:Geometry` |
| **TMP** | TEMPORAL | `crm:E52_Time-Span` |
| **APP** | APPELLATION | `crm:E41_Appellation` |
| **ROL** | ROLE | `org:Role` |
| **WRK** | WORK | `frbroo:F1_Work` |
| **QTY** | QUANTITY | `crm:E54_Dimension` |
**Heritage Institution Subtype**:
```yaml
GRP.HER: # Heritage Custodian
subtypes:
- GRP.HER.GAL # Gallery
- GRP.HER.LIB # Library
- GRP.HER.ARC # Archive
- GRP.HER.MUS # Museum
# ... (matches GLAMORCUBESFIXPHDNT taxonomy)
```
**When Using CH-Annotator, Reference in Provenance**:
```yaml
provenance:
data_source: CONVERSATION_NLP
extraction_method: ch_annotator-v1_7_0 # ← Convention ID
extraction_date: "2025-12-06T10:00:00Z"
confidence_score: 0.92
```
**Claim Provenance Model** (5 required components):
```yaml
claim:
claim_type: full_name
claim_value: "Rijksmuseum Amsterdam"
provenance:
namespace: skos # 1. Ontology prefix
path: /html/body/h1[1] # 2. XPath/JSONPath
timestamp: "2025-12-06T10:00:00Z" # 3. ISO 8601
agent: ch_annotator-v1_7_0 # 4. Extraction model
context_convention: ch_annotator-v1_7_0 # 5. Convention version
```
**Digital Humanities Authority Stack** (Primary):
- TEI P5 (document structure, names)
- CIDOC-CRM 7.1.3 (cultural heritage modeling)
- TimeML/TIMEX3 (temporal expressions)
- FRBR/LRM (bibliographic entities)
- GeoSPARQL (spatial geometry)
**NERD Status**: DEPRECATED for semantic precision. Retained ONLY for NLP pipeline interchange.
**See**:
- `.opencode/CH_ANNOTATOR_CONVENTION.md` for complete documentation
- `data/entity_annotation/ch_annotator-v1_7_0.yaml` for full convention
- `data/entity_annotation/modules/` for modular hypernym definitions
---
### Rule 11: Z.AI GLM API for LLM Tasks (NOT BigModel)
**CRITICAL: When using GLM models in scripts, use the Z.AI Coding Plan endpoint, NOT the regular BigModel API.**
The project uses the same Z.AI Coding Plan that OpenCode uses internally. The regular BigModel API (`open.bigmodel.cn`) will NOT work with our tokens.
**Correct API Configuration**:
| Property | Value |
|----------|-------|
| **API URL** | `https://api.z.ai/api/coding/paas/v4/chat/completions` |
| **Environment Variable** | `ZAI_API_TOKEN` |
| **Recommended Model** | `glm-4.6` |
| **Cost** | Free (0 per token for all GLM models) |
**Available Models**: `glm-4.5`, `glm-4.5-air`, `glm-4.5-flash`, `glm-4.5v`, `glm-4.6`
**Python Implementation**:
```python
import os
import httpx
# CORRECT - Z.AI Coding Plan endpoint
API_URL = "https://api.z.ai/api/coding/paas/v4/chat/completions"
api_key = os.environ.get("ZAI_API_TOKEN")
client = httpx.AsyncClient(
timeout=60.0,
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
}
)
# WRONG - This will fail with quota errors!
# API_URL = "https://open.bigmodel.cn/api/paas/v4/chat/completions"
# api_key = os.environ.get("ZHIPU_API_KEY")
```
**Integration with CH-Annotator**: When using GLM for entity recognition, always reference CH-Annotator v1.7.0 in prompts:
```python
PROMPT = """You are following CH-Annotator v1.7.0 convention.
Heritage institutions are type GRP.HER with subtypes:
- GRP.HER.MUS (museums)
- GRP.HER.LIB (libraries)
- GRP.HER.ARC (archives)
- GRP.HER.GAL (galleries)
..."""
```
**Token Location**:
1. **Environment**: Add `ZAI_API_TOKEN` to `.env` file
2. **OpenCode Auth**: Token stored in `~/.local/share/opencode/auth.json` under key `zai-coding-plan`
**See**: `.opencode/ZAI_GLM_API_RULES.md` for complete documentation
---
### Rule 12: Person Data Reference Pattern - Avoid Inline Duplication
**🚨 CRITICAL: When person profile data is stored in `data/custodian/person/`, custodian files MUST reference the file path instead of duplicating the full profile inline.**
This pattern:
- Reduces data duplication (50+ lines → 5 lines per person)
- Ensures single-source-of-truth for person data
- Enables cross-custodian references (same person at multiple institutions)
- Makes updates easier to manage
**Directory Structure**:
```
data/custodian/
├── person/ # Canonical person profile storage
│ ├── alexandr-belov-bb547b46_20251210T120000Z.json
│ ├── giovanna-fossati_20251209T170000Z.json
│ └── ...
├── NL-NH-AMS-U-EFM-eye_filmmuseum.yaml # References person files
└── ...
```
**File Naming Convention**: `{linkedin-slug}_{ISO-timestamp}.json`
**✅ CORRECT - Reference Pattern**:
```yaml
collection_management_specialist:
- name: Alexandr Belov
role: Collection/Information Specialist
linkedin_url: https://www.linkedin.com/in/alexandr-belov-bb547b46
current: true
person_profile_path: data/custodian/person/alexandr-belov-bb547b46_20251210T120000Z.json
```
**❌ WRONG - Inline Duplication** (50+ lines of profile data inline when file exists):
```yaml
collection_management_specialist:
- name: Alexandr Belov
role: Collection/Information Specialist
linkedin_url: https://www.linkedin.com/in/alexandr-belov-bb547b46
about: "60+ lines of profile data..."
skills: [...]
languages: [...]
career_history: [...]
# ... etc
```
**When to Use Each Pattern**:
| Use File Reference When | Use Inline Data When |
|------------------------|---------------------|
| Full profile already extracted and saved | Only basic info available |
| Person has extensive career history | Minimal profile data |
| Profile data exceeds ~10 lines | Quick placeholder entry |
| Same person referenced by multiple custodians | Before full extraction |
**See**: `.opencode/PERSON_DATA_REFERENCE_PATTERN.md` for complete documentation
---
### Rule 13: Custodian Type Annotations on LinkML Schema Elements
**All LinkML schema elements (classes, slots, enums) MUST be annotated with their applicable GLAMORCUBESFIXPHDNT custodian type codes using the `annotations` block.**
This convention enables:
- **Visual categorization** in UML diagrams (cube face highlighting)
- **Semantic filtering** by heritage institution type
- **Validation** of slot/enum applicability to custodian types
**Annotation Keys**:
| Key | Type | Required | Description |
|-----|------|----------|-------------|
| `custodian_types` | list[string] | YES | List of applicable type codes (e.g., `[A, L]`) |
| `custodian_types_rationale` | string | NO | Explanation of why these types apply |
| `custodian_types_primary` | string | NO | Primary type if multiple apply |
**Type Codes** (single letters from GLAMORCUBESFIXPHDNT):
| Code | Type | Code | Type |
|------|------|------|------|
| G | Gallery | F | Feature custodian |
| L | Library | I | Intangible heritage |
| A | Archive | X | Mixed types |
| M | Museum | P | Personal collection |
| O | Official institution | H | Holy/sacred site |
| R | Research center | D | Digital platform |
| C | Corporation | N | NGO |
| U | Unknown | T | Taste/smell heritage |
| B | Botanical/Zoo | | |
| E | Education provider | | |
| S | Collecting society | | |
**Example - Class Annotation**:
```yaml
classes:
ArchivalFonds:
class_uri: rico:RecordSet
description: An archival fonds representing a collection of records
annotations:
custodian_types: [A]
custodian_types_rationale: "Archival fonds are specific to archive institutions"
```
**Example - Slot Annotation**:
```yaml
slots:
arrangement_system:
description: The archival arrangement system used
range: ArrangementSystemEnum
annotations:
custodian_types: [A]
custodian_types_rationale: "Arrangement systems are an archival concept"
```
**Example - Enum Value Annotation**:
```yaml
enums:
CollectionTypeEnum:
permissible_values:
ARCHIVAL_FONDS:
description: A complete archival fonds
annotations:
custodian_types: [A]
```
**Universal Types**: Use `["*"]` for elements that apply to all custodian types:
```yaml
slots:
preferred_label:
annotations:
custodian_types: ["*"]
```
**Validation Rules**:
1. Values MUST be valid single-letter GLAMORCUBESFIXPHDNT codes
2. Child class types MUST be subset of or equal to parent class types
3. Slot annotations should align with using class annotations
**See**: `.opencode/CUSTODIAN_TYPE_ANNOTATION_CONVENTION.md` for complete documentation
---
### Rule 14: Exa MCP LinkedIn Profile Extraction
**When extracting LinkedIn profile data for heritage custodian staff, use the `exa_crawling_exa` tool with direct profile URL for comprehensive extraction.**
**Tool Selection**:
| Scenario | Tool | Parameters |
|----------|------|------------|
| Profile URL known | `exa_crawling_exa` | `url`, `maxCharacters: 10000` |
| Profile URL unknown | `exa_linkedin_search_exa` | `query`, `searchType: "profiles"` |
| Fallback search | `exa_web_search_exa` | `query: "site:linkedin.com/in/ {name}"` |
**Preferred Workflow** (when LinkedIn URL is available):
```
1. Use exa_crawling_exa with direct URL
↓
2. Extract comprehensive profile data
↓
3. Save to data/custodian/person/{linkedin-slug}_{timestamp}.json
↓
4. Update custodian file with person_profile_path reference
```
**Example - Direct URL Extraction**:
```
Tool: exa_crawling_exa
Parameters:
url: "https://www.linkedin.com/in/alexandr-belov-bb547b46"
maxCharacters: 10000
```
**Why `exa_crawling_exa` over `exa_linkedin_search_exa`**:
- Returns **complete** career history with dates, durations, company metadata
- Includes **all** education entries with institutions and degrees
- Captures **full** about section text
- Returns **profile image URL**
- More reliable than search (which may return irrelevant profiles)
**Output Location**: `data/custodian/person/{linkedin-slug}_{ISO-timestamp}.json`
**Required JSON Fields**:
- `exa_search_metadata` - Tool, timestamp, request ID, cost
- `linkedin_profile_url` - Source URL
- `profile_data.career_history[]` - Complete work history
- `profile_data.education[]` - All education entries
- `profile_data.heritage_relevant_experience[]` - Tagged heritage roles
**See**: `.opencode/EXA_LINKEDIN_EXTRACTION_RULES.md` for complete documentation
---
### Rule 15: Connection Data Registration - Full Network Preservation
**🚨 CRITICAL: When connection data is manually recorded for a person, ALL connections MUST be fully registered in a dedicated connections file in `data/custodian/person/`.**
This rule ensures complete preservation of professional network data, enabling heritage sector network analysis and cross-custodian relationship discovery.
**Connection File Naming Convention**:
```
{linkedin-slug}_connections_{ISO-timestamp}.json
```
**Examples**:
```
alexandr-belov-bb547b46_connections_20251210T160000Z.json
giovanna-fossati_connections_20251211T140000Z.json
```
**Required Connection File Structure**:
```json
{
"source_metadata": {
"source_url": "https://www.linkedin.com/search/results/people/?...",
"scraped_timestamp": "2025-12-10T16:00:00Z",
"scrape_method": "manual_linkedin_browse",
"target_profile": "{linkedin-slug}",
"target_name": "Full Name",
"connections_extracted": 107
},
"connections": [
{
"name": "Connection Name",
"degree": "1st" | "2nd" | "3rd+",
"headline": "Current role or description",
"location": "City, Region, Country",
"organization": "Primary organization",
"heritage_relevant": true | false,
"heritage_type": "A" | "L" | "M" | "R" | "E" | "D" | "G" | etc.
}
],
"network_analysis": {
"total_connections_extracted": 107,
"heritage_relevant_count": 56,
"heritage_relevant_percentage": 52.3,
"connections_by_heritage_type": {...}
}
}
```
**Minimum Required Fields per Connection**:
| Field | Type | Description |
|-------|------|-------------|
| `name` | string | Full name of connection |
| `degree` | string | Connection degree: `1st`, `2nd`, `3rd+` |
| `headline` | string | Current role/description |
| `heritage_relevant` | boolean | Is this person in heritage sector? |
**Heritage Type Codes**: Use single-letter GLAMORCUBESFIXPHDNT codes (G, L, A, M, O, R, C, U, B, E, S, F, I, X, P, H, D, N, T).
**Referencing from Custodian Files**:
```yaml
collection_management_specialist:
- name: Alexandr Belov
role: Collection/Information Specialist
linkedin_url: https://www.linkedin.com/in/alexandr-belov-bb547b46
current: true
person_profile_path: data/custodian/person/alexandr-belov-bb547b46_20251210T120000Z.json
person_connections_path: data/custodian/person/alexandr-belov-bb547b46_connections_20251210T160000Z.json
```
**Why This Rule Matters**:
1. **Complete Data Preservation**: Connections are expensive to extract (manual scraping, rate limits)
2. **Heritage Sector Mapping**: Understanding who knows whom in the heritage community
3. **Cross-Custodian Discovery**: Find staff who work at multiple institutions
4. **Network Analysis**: Identify key influencers and knowledge hubs
**See**: `.opencode/CONNECTION_DATA_REGISTRATION_RULE.md` for complete documentation including network analysis sections
---
### Rule 16: LinkedIn Photo URLs - Store CDN URLs, Not Overlay Pages
**🚨 CRITICAL: When storing LinkedIn profile photo URLs, store the ACTUAL CDN image URL from `media.licdn.com`, NOT the overlay page URL.**
The overlay page URL (`/overlay/photo/`) is trivially derivable from any LinkedIn profile URL and provides zero informational value. The actual CDN URL requires extraction effort and is the only URL that directly serves the image.
**URL Patterns**:
| Type | Pattern | Value |
|------|---------|-------|
| ❌ **WRONG** (overlay page) | `https://www.linkedin.com/in/{slug}/overlay/photo/` | Derivable, useless |
| ✅ **CORRECT** (CDN image) | `https://media.licdn.com/dms/image/v2/{ID}/profile-displayphoto-shrink_800_800/...` | Actual image |
**CDN URL Structure**:
```
https://media.licdn.com/dms/image/v2/{image_id}/profile-displayphoto-shrink_{size}/{encoded_params}
```
Where `{size}` can be: `100_100`, `200_200`, `400_400`, `800_800`
**Person Profile JSON Schema**:
```json
{
"linkedin_profile_url": "https://www.linkedin.com/in/giovannafossati",
"linkedin_photo_url": "https://media.licdn.com/dms/image/v2/C4D03AQHQCBcoih82SQ/profile-displayphoto-shrink_800_800/...",
"photo_urls": null
}
```
**When CDN URL is Not Available** (profile viewed without login, or no photo):
```json
{
"linkedin_profile_url": "https://www.linkedin.com/in/example-person",
"linkedin_photo_url": null,
"photo_urls": {
"primary": "https://example.org/staff/person-headshot.jpg",
"sources": [
{
"url": "https://example.org/staff/person-headshot.jpg",
"source": "Institutional website",
"retrieved_date": "2025-12-09"
}
]
}
}
```
**Extraction Methods** (in order of preference):
1. **Browser DevTools** - Inspect `<img>` element in profile photo overlay, copy `src` attribute
2. **Exa Crawling** - Use `exa_crawling_exa` tool with profile URL, extract from returned content
3. **Alternative Sources** - If LinkedIn CDN unavailable, use institutional websites, conference photos, etc.
**Rationale**:
- Overlay URL = `profile_url + "/overlay/photo/"` → No information gain
- CDN URL contains unique image identifiers → Actual data
- CDN URLs can be used to display images directly
- Multiple size variants available by changing size parameter
**See**:
- `.opencode/LINKEDIN_PHOTO_CDN_RULE.md` for agent rule reference
- `docs/LINKEDIN_PHOTO_URL_EXTRACTION.md` for complete extraction documentation
---
### Rule 17: LinkedIn Connection Unique Identifiers - Include Abbreviated and Anonymous Names
**🚨 CRITICAL: When parsing LinkedIn connections, EVERY connection MUST receive a unique `connection_id`, including abbreviated names (e.g., "Amy B.") and anonymous entries (e.g., "LinkedIn Member").**
This rule ensures complete data preservation for heritage sector network analysis, even when LinkedIn privacy settings obscure full names.
**Connection ID Format**:
```
{target_slug}_conn_{index:04d}_{name_slug}
```
**Examples**:
```
elif-rongen-kaynakci-35295a17_conn_0042_amy_b
giovannafossati_conn_0156_linkedin_member
anne-gant-59908a18_conn_0003_tina_m_bastajian
```
**Name Type Classification**:
| Type | Pattern | Example | `name_type` Value |
|------|---------|---------|-------------------|
| **Full Name** | First + Last name | "John Smith" | `full` |
| **Abbreviated** | Contains single initial | "Amy B.", "S. Buse Yildirim", "Tina M. Bastajian" | `abbreviated` |
| **Anonymous** | Privacy-hidden profile | "LinkedIn Member" | `anonymous` |
**Required Fields per Connection**:
```json
{
"connection_id": "target-slug_conn_0042_amy_b",
"name": "Amy B.",
"name_type": "abbreviated",
"degree": "2nd",
"headline": "Film Archivist at EYE Filmmuseum",
"location": "Amsterdam, Netherlands",
"heritage_relevant": true,
"heritage_type": "A"
}
```
**Abbreviated Name Detection Patterns**:
```python
# Single letter followed by period
"Amy B." # Last name abbreviated
"S. Buse Yildirim" # First name abbreviated
"Tina M. Bastajian" # Middle initial
"İ. Can Koç" # Turkish first initial
```
**Name Slug Generation Rules**:
1. Normalize unicode (NFD decomposition)
2. Remove diacritics (é→e, ö→o, ñ→n, İ→i)
3. Convert to lowercase
4. Replace non-alphanumeric with underscores
5. Collapse multiple underscores
6. Truncate to 30 characters maximum
**Implementation Script**: `scripts/parse_linkedin_connections.py`
**Key Functions**:
- `is_abbreviated_name(name)` - Detects single-letter initials
- `is_anonymous_name(name)` - Detects "LinkedIn Member" patterns
- `generate_connection_id(name, index, target_slug)` - Creates unique IDs
**Usage**:
```bash
python scripts/parse_linkedin_connections.py \
data/custodian/person/manual_register/{slug}_connections_{timestamp}.md \
data/custodian/person/{slug}_connections_{timestamp}.json \
--target-name "Full Name" \
--target-slug "linkedin-slug"
```
**Why This Rule Matters**:
1. **Deduplication**: Same abbreviated name across different connection lists can be linked
2. **Privacy Respect**: Preserves privacy while enabling analysis
3. **Complete Data**: No connections are silently dropped
4. **Network Analysis**: Enables heritage sector relationship mapping even with partial data
**Output Statistics** (from real extractions):
| Person | Total | Full Names | Abbreviated | Anonymous |
|--------|-------|------------|-------------|-----------|
| Elif Rongen-Kaynakçi | 475 | 449 (94.5%) | 26 (5.5%) | 0 |
| Giovanna Fossati | 776 | 746 (96.1%) | 30 (3.9%) | 0 |
**Understanding Duplicates in Raw Connection Data**:
**🚨 IMPORTANT**: Duplicates in raw manually-registered connection data are **expected and normal**, not a data quality issue.
When manually registering LinkedIn connections, the **same person can appear multiple times** because:
1. **Connection degree is relative to the VIEWER (person conducting the search), NOT the target profile being analyzed**
2. In a social network graph, **multiple paths of different lengths can exist to the same node** - a person can be your 1st degree (direct connection) AND also reachable as 2nd degree via another connection
3. LinkedIn may display different degree values for the same person depending on which path it evaluates
**Key Constraint**: The `degree` field (1st, 2nd, 3rd+) reflects the relationship between the **viewer** and the **connection**, NOT the relationship between the target profile and the connection. This is a LinkedIn UI limitation.
**Example**: If you (Alice) are browsing Bob's connections and see Carol listed as "2nd", that means Carol is YOUR 2nd degree connection, not Bob's.
**Processing**: Raw MD files typically have 15-25% duplicates. Deduplicate by name when creating final JSON output.
**See**: `.opencode/LINKEDIN_CONNECTION_ID_RULE.md` for complete documentation
---
### Rule 18: Custodian Staff Parsing from LinkedIn Company Pages
**When manually registering heritage custodian staff from LinkedIn company "People" pages, use the `parse_custodian_staff.py` script to convert raw text files into structured JSON.**
**File Locations**:
| Type | Location |
|------|----------|
| **Raw input files** | `data/custodian/person/manual_hc/{slug}-{timestamp}.md` |
| **Parsed output files** | `data/custodian/person/{slug}_staff_{timestamp}.json` |
| **Parser script** | `scripts/parse_custodian_staff.py` |
**Usage**:
```bash
python scripts/parse_custodian_staff.py <input_file> <output_file> \
--custodian-name "Custodian Name" \
--custodian-slug "custodian-slug"
```
**Example**:
```bash
python scripts/parse_custodian_staff.py \
data/custodian/person/manual_hc/collectie_overijssel-20251210T0055.md \
data/custodian/person/collectie_overijssel_staff_20251210T0055.json \
--custodian-name "Collectie Overijssel" \
--custodian-slug "collectie-overijssel"
```
**Output Structure**:
```json
{
"custodian_metadata": {
"custodian_name": "Collectie Overijssel",
"industry": "Museums, Historical Sites, and Zoos",
"location": { "city": "Zwolle", "region": "Overijssel" },
"employee_count": "51-200",
"associated_members": 58
},
"staff": [
{
"staff_id": "collectie-overijssel_staff_0000_annelien_vos_keen",
"name": "Annelien Vos-Keen",
"name_type": "full",
"degree": "2nd",
"headline": "Data Analist / KPI- en procesexpert",
"mutual_connections": "Thomas van Maaren, Bob Coret, and 4 other mutual connections",
"heritage_relevant": true,
"heritage_type": "D"
}
],
"staff_analysis": {
"total_staff_extracted": 39,
"heritage_relevant_count": 25,
"heritage_relevant_percentage": 64.1,
"staff_by_heritage_type": { "A": 4, "D": 1, "M": 18 }
}
}
```
**Staff ID Format**: `{custodian_slug}_staff_{index:04d}_{name_slug}`
**Heritage Type Detection**: Staff headlines are analyzed using GLAMORCUBESFIXPHDNT keywords:
| Code | Type | Keywords |
|------|------|----------|
| A | Archive | archief, archivist, archivaris, nationaal archief |
| M | Museum | museum, curator, conservator, collectie |
| D | Digital | digital, data, developer, digitalisering |
| E | Education | university, professor, docent, educatie |
**Existing Files**:
| Custodian | Staff | Heritage-Relevant |
|-----------|-------|-------------------|
| Collectie Overijssel | 39 | 25 (64%) |
| Nationaal Archief | 373 | 268 (72%) |
**Relationship to Rule 15 & 17**: This script complements the connection parsing scripts:
| Script | Purpose |
|--------|---------|
| `parse_linkedin_connections.py` | Parse PERSON's connections |
| `parse_custodian_staff.py` | Parse ORGANIZATION's staff |
**See**: `.opencode/CUSTODIAN_STAFF_PARSING_RULE.md` for complete documentation
---
### Rule 19: HTML-Only LinkedIn Extraction (Preferred Method)
**Use ONLY manually saved HTML files (not MD copy-paste) for extracting LinkedIn data.**
When extracting people data from LinkedIn (custodian staff, person connections), the HTML source contains 100% of the data. Markdown copy-paste loses critical information (especially profile URLs) and should NOT be used as a primary source.
**Data Completeness Comparison**:
| Data Source | Profile URLs | Headlines | Names | Connection Degree | Coverage |
|-------------|--------------|-----------|-------|-------------------|----------|
| **HTML (saved page)** | 100% | 100% | 100% | 100% | **100%** |
| **MD (copy-paste)** | 0% | 90% | 100% | 90% | ~90% |
**Primary Script**: `scripts/parse_linkedin_html.py`
**Usage**:
```bash
python scripts/parse_linkedin_html.py \
"data/custodian/person/manual_hc/Rijksmuseum_ People _ LinkedIn.html" \
--custodian-name "Rijksmuseum" \
--custodian-slug "rijksmuseum" \
--output data/custodian/person/rijksmuseum_staff_20251211T000000Z.json
```
**Key Features**:
- Extracts complete profile data including LinkedIn profile URLs (`linkedin.com/in/{slug}`)
- Handles anonymous "LinkedIn Member" entries (privacy-protected profiles)
- Deduplicates regular profiles by name but preserves each anonymous entry
- Detects heritage-relevant staff using GLAMORCUBESFIXPHDNT keywords
**How to Save LinkedIn HTML**:
1. Navigate to company's LinkedIn page (e.g., `linkedin.com/company/rijksmuseum/people/`)
2. **Scroll down to load ALL profiles** (LinkedIn uses infinite scroll)
3. **File > Save Page As...** > "Webpage, Complete"
4. Save to `data/custodian/person/manual_hc/`
**Note on Count Discrepancies**: The "associated members" count in the page header may differ slightly from extracted count (typically within 1-3%) because:
- Header count updates asynchronously
- HTML contains actual rendered profiles which may be more current
**See**: `.opencode/HTML_ONLY_LINKEDIN_EXTRACTION_RULE.md` for complete documentation
---
## Project Overview
**Goal**: Extract structured data about worldwide GLAMORCUBESFIXPHDNT (Galleries, Libraries, Archives, Museums, Official institutions, Research centers, Corporations, Unknown, Botanical gardens/zoos, Educational providers, Societies, Features, Intangible heritage groups, miXed, Personal collections, Holy sites, Digital platforms, NGOs, Taste/smell heritage) institutions from 139+ Claude conversation JSON files and integrate with authoritative CSV datasets.
**Output**: Validated LinkML-compliant records representing heritage custodian organizations with provenance tracking, geographic data, identifiers, and relationship information.
**Schema**: See the modular LinkML schema v0.2.1 with 19-type GLAMORCUBESFIXPHDNT taxonomy described below.
## Schema Reference (v0.2.1)
The project uses a **modular LinkML schema** organized into 6 specialized modules:
1. **`schemas/heritage_custodian.yaml`** - Main schema (import-only structure)
- Top-level schema that imports all modules
- Defines schema metadata and namespace
2. **`schemas/core.yaml`** - Core Classes
- `HeritageCustodian` - Main institution entity
- `Location` - Geographic data
- `Identifier` - External identifiers (ISIL, Wikidata, VIAF, etc.)
- `DigitalPlatform` - Online systems and platforms
- `GHCID` - Global Heritage Custodian Identifier
3. **`schemas/enums.yaml`** - Enumerations
- `InstitutionTypeEnum` - 13 institution types (GALLERY, LIBRARY, ARCHIVE, MUSEUM, etc.)
- `ChangeTypeEnum` - 11 organizational change types (FOUNDING, MERGER, CLOSURE, etc.)
- `DataSource` - Data origin types (CSV_REGISTRY, CONVERSATION_NLP, etc.)
- `DataTier` - Data quality tiers (TIER_1_AUTHORITATIVE through TIER_4_INFERRED)
- `PlatformTypeEnum` - Digital platform categories
4. **`schemas/provenance.yaml`** - Provenance Tracking
- `Provenance` - Data source and quality metadata
- `ChangeEvent` - Organizational change history (mergers, relocations, etc.)
- `GHCIDHistoryEntry` - GHCID change tracking over time
5. **`schemas/collections.yaml`** - Collection Metadata
- `Collection` - Collection descriptions
- `Accession` - Acquisition records
- `DigitalObject` - Digital surrogates
6. **`schemas/dutch.yaml`** - Dutch-Specific Extensions
- `DutchHeritageCustodian` - Netherlands heritage institutions
- Extensions for ISIL registry, platform integrations, KvK numbers
See `/docs/SCHEMA_MODULES.md` for detailed architecture and design patterns.
## Base Ontologies for Global GLAM Data
**CRITICAL**: Before designing extraction pipelines or extending the schema, AI agents MUST consult the base ontologies that the LinkML schema builds upon. These ontologies provide standardized vocabularies and patterns for modeling heritage institutions.
### Foundation Ontologies
The GLAM project integrates with three primary ontologies, each serving different geographic and semantic scopes:
#### 1. TOOI - Dutch Government Organizational Ontology
**File**: `/data/ontology/tooiont.ttl`
**Namespace**: `https://identifier.overheid.nl/tooi/def/ont/`
**Scope**: Dutch heritage institutions (government archives, state museums, public cultural organizations)
**When to Use**:
- ✅ Extracting Dutch heritage institutions from conversations
- ✅ Modeling Dutch organizational change events (mergers, splits, reorganizations)
- ✅ Integrating with Dutch ISIL registry or KvK (Chamber of Commerce) data
- ✅ Parsing Dutch government heritage agency data
**Key Classes**:
- `tooi:Overheidsorganisatie` - Government organization (extends to `DutchHeritageCustodian`)
- `tooi:Wijzigingsgebeurtenis` - Change event (founding, merger, closure, relocation)
**Key Properties**:
- `tooi:officieleNaamInclSoort` - Official name including type
- `tooi:begindatum` / `tooi:einddatum` - Temporal validity (start/end dates)
- `tooi:organisatieIdentificatie` - Formal identifiers (ISIL codes, etc.)
**LinkML Mapping**:
```yaml
# schemas/dutch.yaml extends TOOI
DutchHeritageCustodian:
is_a: HeritageCustodian
class_uri: tooi:Overheidsorganisatie # ← Maps to TOOI
```
**Reference**: See `/docs/ONTOLOGY_EXTENSIONS.md` for complete TOOI integration patterns.
---
#### 2. CPOV - EU Core Public Organisation Vocabulary
**Files**:
- `/data/ontology/core-public-organisation-ap.ttl` (RDF schema)
- `/data/ontology/core-public-organisation-ap.jsonld` (JSON-LD context)
**Namespace**: `http://data.europa.eu/m8g/`
**Scope**: EU-wide and global public sector heritage organizations
**When to Use**:
- ✅ Extracting European heritage institutions (France, Germany, Belgium, etc.)
- ✅ Modeling international/global heritage organizations
- ✅ Aligning with EU Linked Open Data initiatives (Europeana, DPLA)
- ✅ Extracting non-Dutch institutions from conversations
**Key Classes**:
- `cpov:PublicOrganisation` - Public sector organization (base for `HeritageCustodian`)
- `cv:ChangeEvent` - Organizational change events
- `locn:Address` - Physical location data
**Key Properties**:
- `skos:prefLabel` / `skos:altLabel` - Preferred and alternative names
- `dct:identifier` - Formal identifiers (ISIL, Wikidata, VIAF)
- `dct:temporal` - Temporal coverage (founding to closure dates)
- `locn:address` - Physical addresses
**LinkML Mapping**:
```yaml
# schemas/core.yaml aligns with CPOV
HeritageCustodian:
class_uri: cpov:PublicOrganisation # ← Maps to CPOV
slots:
name:
slot_uri: skos:prefLabel
alternative_names:
slot_uri: skos:altLabel
identifiers:
slot_uri: dct:identifier
```
**Reference**: See `/docs/ONTOLOGY_EXTENSIONS.md` for complete CPOV integration patterns.
---
#### 3. Schema.org - Web Vocabulary for Structured Data
**File**: `/data/ontology/schemaorg.owl`
**Namespace**: `http://schema.org/`
**Scope**: Universal web semantics (museums, galleries, collections, events, learning resources)
**When to Use**:
- ✅ Extracting private collections or non-governmental organizations
- ✅ Modeling digital platforms (learning management systems, discovery portals)
- ✅ Web discoverability and SEO optimization
- ✅ Fallback when TOOI/CPOV don't apply
**Key Classes**:
- `schema:Museum` / `schema:Library` / `schema:ArchiveOrganization` - Heritage institution types
- `schema:Place` - Geographic locations
- `schema:LearningResource` - Educational platforms (LMS, online courses)
- `schema:Event` - Organizational events (founding, exhibitions)
**LinkML Mapping**:
```yaml
# schemas/enums.yaml maps platform types to Schema.org
DigitalPlatformTypeEnum:
LEARNING_MANAGEMENT:
meaning: schema:LearningResource # ← Maps to Schema.org
```
**Reference**: See `/docs/ONTOLOGY_EXTENSIONS.md` for Schema.org usage examples.
---
### Ontology Decision Tree for Agents
When extracting heritage institution data, choose the appropriate ontology:
```
START: Extract institution from conversation
↓
Is the institution Dutch?
├─ YES → Use TOOI ontology
│ - Map to schemas/dutch.yaml
│ - Extract ISIL codes (NL-* format)
│ - Extract KvK numbers (8-digit)
│ - Model change events as tooi:Wijzigingsgebeurtenis
│
└─ NO → Is it a public/government organization?
├─ YES → Use CPOV ontology
│ - Map to schemas/core.yaml
│ - Extract standard identifiers (ISIL, Wikidata, VIAF)
│ - Model change events as cv:ChangeEvent
│
└─ NO → Use Schema.org
- Map to schemas/core.yaml
- Use schema:Museum, schema:Library, etc.
- Emphasize web discoverability
```
**Multi-Ontology Support**: Institutions can implement MULTIPLE ontology classes simultaneously:
```turtle
<https://w3id.org/heritage/custodian/nl/rijksmuseum>
a tooi:Overheidsorganisatie, # Dutch government organization
cpov:PublicOrganisation, # EU public sector
schema:Museum ; # Schema.org web semantics
```
---
### Required Ontology Consultation Workflow
**Before extracting data**, agents MUST perform these steps:
#### Step 1: Identify Institution Geographic Scope
```bash
# Determine which ontology applies
if institution_country == "NL":
primary_ontology = "TOOI"
ontology_file = "/data/ontology/tooiont.ttl"
elif institution_in_europe or institution_public_sector:
primary_ontology = "CPOV"
ontology_file = "/data/ontology/core-public-organisation-ap.ttl"
else:
primary_ontology = "Schema.org"
ontology_file = "/data/ontology/schemaorg.owl"
```
#### Step 2: Review Ontology Classes and Properties
**Search ontology files** for relevant classes:
```bash
# Dutch institutions - search TOOI
rg "tooi:Overheidsorganisatie|Wijzigingsgebeurtenis|begindatum" /data/ontology/tooiont.ttl
# EU/global institutions - search CPOV
rg "cpov:PublicOrganisation|cv:ChangeEvent|locn:Address" /data/ontology/core-public-organisation-ap.ttl
# All institutions - search Schema.org
rg "schema:Museum|schema:Library|schema:ArchiveOrganization" /data/ontology/schemaorg.owl
```
#### Step 3: Map Conversation Data to Ontology Properties
Create a mapping table before extraction:
| Extracted Field | TOOI Property | CPOV Property | Schema.org Property |
|-----------------|---------------|---------------|---------------------|
| Institution name | `tooi:officieleNaamInclSoort` | `skos:prefLabel` | `schema:name` |
| Alternative names | - | `skos:altLabel` | `schema:alternateName` |
| Founding date | `tooi:begindatum` | `schema:startDate` | `schema:foundingDate` |
| Closure date | `tooi:einddatum` | `schema:endDate` | `schema:dissolutionDate` |
| ISIL code | `tooi:organisatieIdentificatie` | `dct:identifier` | `schema:identifier` |
| Address | (use `locn:Address`) | `locn:address` | `schema:address` |
| Merger event | `tooi:Wijzigingsgebeurtenis` | `cv:ChangeEvent` | `schema:Event` |
| Website | - | `schema:url` | `schema:url` |
#### Step 4: Document Ontology Alignment in Provenance
**Always include** ontology references in extraction metadata:
```yaml
provenance:
data_source: CONVERSATION_NLP
extraction_method: "NLP extraction following CPOV ontology patterns"
base_ontology: "http://data.europa.eu/m8g/" # ← Document which ontology used
ontology_alignment:
- "cpov:PublicOrganisation"
- "cv:ChangeEvent"
extraction_date: "2025-11-09T..."
```
---
### Common Ontology Patterns
**Pattern 1: Organizational Change Events**
When extracting mergers, splits, relocations, name changes:
```yaml
# TOOI pattern (Dutch institutions)
change_history:
- event_id: https://w3id.org/heritage/custodian/event/nha-merger-2001
change_type: MERGER # Maps to tooi:Wijzigingsgebeurtenis
event_date: "2001-01-01"
event_description: "Merger of Gemeentearchief Haarlem and Rijksarchief in Noord-Holland"
ontology_class: "tooi:Wijzigingsgebeurtenis"
# CPOV pattern (EU/global institutions)
change_history:
- event_id: https://w3id.org/heritage/custodian/event/bnf-founding
change_type: FOUNDING # Maps to cv:ChangeEvent
event_date: "1461-01-01"
event_description: "Founded by King Louis XI as Royal Library"
ontology_class: "cv:ChangeEvent"
```
**Pattern 2: Multilingual Names**
CPOV and Schema.org support language-tagged literals:
```yaml
name: Bibliothèque nationale de France
alternative_names:
- National Library of France@en
- BnF@fr
- Französische Nationalbibliothek@de
# RDF serialization:
# skos:prefLabel "Bibliothèque nationale de France"@fr ;
# skos:altLabel "National Library of France"@en, "BnF"@fr ;
```
**Pattern 3: Hierarchical Relationships**
Use W3C Org Ontology patterns (integrated in CPOV):
```yaml
# Parent institution
parent_organization:
name: Ministry of Culture
relationship_type: "org:hasUnit" # CPOV uses W3C Org Ontology
# Branch institutions
branches:
- name: Regional Archive Noord-Brabant
relationship_type: "org:subOrganizationOf"
```
---
### Anti-Patterns to Avoid
**❌ DON'T**: Invent custom properties when ontology equivalents exist
```yaml
# BAD - Custom property instead of ontology reuse
institution_official_name: "Rijksarchief" # Use skos:prefLabel instead!
```
**❌ DON'T**: Ignore ontology namespace conventions
```yaml
# BAD - No ontology reference
change_type: "merger" # Use cv:ChangeEvent with proper namespace!
```
**❌ DON'T**: Extract without reviewing ontology files
```bash
# BAD - Extracting Dutch institutions without reading TOOI
agent: "I'll extract Dutch archives using Schema.org only"
# This loses semantic precision and ignores domain-specific patterns!
```
**✅ DO**: Always map to base ontologies and document alignment
```yaml
# GOOD - Ontology-aligned extraction
name: Rijksarchief in Noord-Holland
institution_type: ARCHIVE
ontology_class: tooi:Overheidsorganisatie # ← Documented
provenance:
base_ontology: "https://identifier.overheid.nl/tooi/def/ont/"
ontology_alignment:
- tooi:Overheidsorganisatie
- prov:Organization # TOOI uses PROV-O for temporal tracking
```
---
### Additional Ontology Resources
**CIDOC-CRM** (Cultural Heritage Domain):
- File: `/data/ontology/CIDOC_CRM_v7.1.3.rdf`
- Use for: Museum object cataloging, provenance, conservation
- Key classes: `crm:E74_Group` (organizations), `crm:E5_Event` (historical events)
**RiC-O** (Records in Contexts - Archival Description):
- Use for: Archival collections, fonds, series, items
- Key classes: `rico:CorporateBody`, `rico:RecordSet`
- Integration: Planned for future schema extension
**BIBFRAME** (Bibliographic Resources):
- Use for: Library catalogs, bibliographic metadata
- Key classes: `bf:Organization`, `bf:Work`, `bf:Instance`
- Integration: For library-specific extensions
**Reference Documentation**: See `/docs/ONTOLOGY_EXTENSIONS.md` for comprehensive integration patterns, RDF serialization examples, and extension workflows.
---
## Institution Type Taxonomy
The project uses a 19-type GLAMORCUBESFIXPHDNT taxonomy (expanded November 2025) with single-letter codes for GHCID identifier generation:
| Type | Code | Description | Example Use Cases |
|------|------|-------------|-------------------|
| **GALLERY** | G | Art gallery or exhibition space | Commercial galleries, kunsthallen |
| **LIBRARY** | L | Library (public, academic, specialized) | National libraries, university libraries |
| **ARCHIVE** | A | Archive (government, corporate, personal) | National archives, city archives |
| **MUSEUM** | M | Museum (art, history, science, etc.) | Rijksmuseum, natural history museums |
| **OFFICIAL_INSTITUTION** | O | Government heritage agencies | Provincial archives, heritage platforms |
| **RESEARCH_CENTER** | R | Research institutes and documentation centers | Knowledge centers, research libraries |
| **CORPORATION** | C | Corporate heritage collections | Company archives, corporate museums |
| **UNKNOWN** | U | Institution type cannot be determined | Ambiguous or unclassifiable organizations |
| **BOTANICAL_ZOO** | B | Botanical gardens and zoological parks | Arboreta, botanical gardens, zoos |
| **EDUCATION_PROVIDER** | E | Educational institutions with collections | Schools, training centers with heritage materials, universities |
| **COLLECTING_SOCIETY** | S | Societies collecting specialized materials | Numismatic societies, heritage societies (heemkundige kring) |
| **FEATURES** | F | Physical landscape features with heritage significance | Monuments, sculptures, statues, memorials, landmarks, cemeteries |
| **INTANGIBLE_HERITAGE_GROUP** | I | Organizations preserving intangible heritage | Traditional performance groups, oral history societies, folklore organizations |
| **MIXED** | X | Multiple types (uses X code) | Combined museum/archive facilities |
| **PERSONAL_COLLECTION** | P | Private personal collections | Individual collectors |
| **HOLY_SITES** | H | Religious heritage sites and institutions | Churches, temples, mosques, synagogues with collections |
| **DIGITAL_PLATFORM** | D | Digital heritage platforms and repositories | Online archives, digital libraries, virtual museums |
| **NGO** | N | Non-governmental heritage organizations | Heritage advocacy groups, preservation societies |
| **TASTE_SMELL** | T | Culinary and olfactory heritage institutions | Historic restaurants, parfumeries, distilleries preserving traditional recipes and formulations |
**Notes**:
- MIXED institutions use "X" as the GHCID code and document all actual types in metadata
- HOLY_SITES includes religious institutions managing cultural heritage collections (archives, libraries, artifacts)
- FEATURES includes physical monuments and landscape features with heritage value (not institutions maintaining collections)
- COLLECTING_SOCIETY includes historical societies (historische vereniging), philatelic societies, numismatic clubs, ephemera collectors
- OFFICIAL_INSTITUTION includes aggregation platforms, provincial heritage services, and government heritage agencies
- INTANGIBLE_HERITAGE_GROUP covers organizations preserving UNESCO-recognized intangible cultural heritage
- DIGITAL_PLATFORM includes born-digital heritage platforms and digitization aggregators
- NGO includes non-profit heritage organizations that don't fit other categories
- TASTE_SMELL includes establishments actively preserving culinary traditions, historic recipes, perfume formulations, and sensory heritage
- When institution type is unknown, records default to UNKNOWN pending verification
**Mnemonic**: **GLAMORCUBESFIXPHDNT** - Galleries, Libraries, Archives, Museums, Official institutions, Research centers, Corporations, Unknown, Botanical gardens/zoos, Education providers, Societies, Features, Intangible heritage groups, miXed, Personal collections, Holy sites, Digital platforms, NGOs, Taste/smell heritage
**Note on order**: The mnemonic GLAMORCUBESFIXPHDNT represents the alphabetical ordering by code: G-L-A-M-O-R-C-U-B-E-S-F-I-X-P-H-D-N-T
**Note**: Universities are classified under **E (EDUCATION_PROVIDER)**, not U. The U-class is reserved for institutions where the type cannot be determined during data extraction.
## Data Sources
### Primary Sources
1. **Conversation JSON files** (`/Users/kempersc/Documents/claude/glam/*.json`)
- 139 conversation files covering global GLAM research
- Countries include: Brazil, Vietnam, Chile, Japan, Mexico, Norway, Thailand, Taiwan, Belgium, Azerbaijan, Estonia, Namibia, Argentina, Tunisia, Ghana, Iran, Russia, Uzbekistan, Armenia, Georgia, Croatia, Greece, Nigeria, Somalia, Yemen, Oman, South Korea, Malaysia, Colombia, Switzerland, Moldova, Romania, Albania, Bosnia, Pakistan, Suriname, Nicaragua, Congo, Denmark, Austria, Australia, Myanmar, Cambodia, Sri Lanka, Tajikistan, Turkmenistan, Philippines, Latvia, Palestine, Limburg (NL), Gelderland (NL), Drenthe (NL), Groningen (NL), Slovakia, Kenya, Paraguay, Honduras, Mozambique, Eritrea, Sudan, Rwanda, Kiribati, Jamaica, Indonesia, Italy, Zimbabwe, East Timor, UAE, Kuwait, Lebanon, Syria, Maldives, Benin
- Also 14 ontology research conversations
2. **Dutch ISIL Registry** (`data/ISIL-codes_2025-08-01.csv`)
- ~300 Dutch heritage institutions
- Fields: Volgnr, Plaats, Instelling, ISIL code, Toegekend op, Opmerking
- Authoritative source (Tier 1)
3. **Dutch Organizations CSV** (`data/voorbeeld_lijst_organisaties_en_diensten-totaallijst_nederland.csv`)
- Comprehensive Dutch heritage organizations
- 40+ metadata columns including: name, address, ISIL code, organization type, partnerships, systems used, metadata standards
- Rich integration data (Museum register, Rijkscollectie, Collectie Nederland, Archieven.nl, etc.)
- Authoritative source (Tier 1)
### Implementation Status (Updated Nov 2025)
Both Dutch datasets have been **successfully parsed and cross-linked**:
**ISIL Registry** ✅:
- 364 institutions parsed (2 invalid codes rejected)
- 203 cities covered
- Parser: `src/glam_extractor/parsers/isil_registry.py`
- Tests: 10/10 passing (84% coverage)
**Dutch Organizations** ✅:
- 1,351 institutions parsed
- 475 cities covered
- 1,119 organizations with digital platforms
- Parser: `src/glam_extractor/parsers/dutch_orgs.py`
- Tests: 18/18 passing (98% coverage)
**Cross-linking Results** 🔗:
- 340 institutions matched by ISIL code (92.1% overlap)
- 198 records enriched with platform data
- 127 name conflicts detected (require manual review)
- 1,004 organizations without ISIL codes (candidates for assignment)
**Analysis Scripts**:
- `compare_dutch_datasets.py` - Dataset comparison
- `crosslink_dutch_datasets.py` - TIER_1 data merging demo
- `test_real_dutch_orgs.py` - Real data validation
See `PROGRESS.md` for detailed statistics and findings.
---
## Conversation JSON Structure
Each conversation JSON file has the following structure:
```json
{
"uuid": "conversation-uuid",
"name": "Conversation name (often includes country/region)",
"summary": "Optional summary",
"created_at": "ISO 8601 timestamp",
"updated_at": "ISO 8601 timestamp",
"chat_messages": [
{
"uuid": "message-uuid",
"text": "User or assistant message text",
"sender": "human" | "assistant",
"content": [
{
"type": "text" | "tool_use" | "tool_result",
"text": "Message content (may contain markdown, lists, etc.)",
...
}
]
}
]
}
```
## NLP Extraction Tasks
All extraction tasks map to the modular LinkML schema v0.2.0. See **Schema Reference** section above for module details.
### Task 1: Entity Recognition - Institution Names
**Objective**: Extract heritage institution names from conversation text.
**Schema Mapping**: Populates `HeritageCustodian` class from `schemas/core.yaml`
**Patterns to Look For**:
- Organization names (proper nouns)
- Museum names (often contain "Museum", "Museu", "Museo", "Muzeum", etc.)
- Library names (contain "Library", "Biblioteca", "Bibliothek", "Bibliotheek", etc.)
- Archive names (contain "Archive", "Archivo", "Archiv", "Archief", etc.)
- Gallery names
- Cultural centers
- Holy sites with collections (churches, temples, mosques, synagogues, monasteries, abbeys, cathedrals managing heritage materials)
**Contextual Indicators**:
- Lists of institutions
- Descriptions like "The X is a museum in Y"
- URLs containing institution names
- Mentions of collections, exhibitions, or holdings
**Example Extraction**:
```markdown
Input: "The Biblioteca Nacional do Brasil in Rio de Janeiro holds over 9 million items..."
Output:
- name: "Biblioteca Nacional do Brasil" # HeritageCustodian.name
- institution_type: LIBRARY # InstitutionTypeEnum from schemas/enums.yaml
- city: "Rio de Janeiro" # Location.city from schemas/core.yaml
- confidence_score: 0.95 # Provenance.confidence_score from schemas/provenance.yaml
```
### Task 2: Location Extraction
**Objective**: Extract geographic information associated with institutions.
**Schema Mapping**: Populates `Location` class from `schemas/core.yaml`
**Extract**:
- City names
- Street addresses (when mentioned)
- Postal codes
- Provinces/states/regions
- Country (can often be inferred from conversation title)
**Geocoding**:
- Use Nominatim API to geocode addresses to lat/lon
- Link to GeoNames IDs when possible
- Handle multilingual place names
**Example**:
```markdown
Input: "Nationaal Onderduikmuseum, Aalten"
Output:
- city: "Aalten" # Location.city
- country: "NL" # Location.country (ISO 3166-1 alpha-2)
- geonames_id: "2759899" (lookup via API) # Location.geonames_id
- latitude: 51.9167 (from geocoding)
- longitude: 6.5833
```
### Task 3: Identifier Extraction
**Objective**: Extract external identifiers mentioned in conversations.
**Schema Mapping**: Populates `Identifier` class from `schemas/core.yaml`
**Identifier Types**:
- ISIL codes (format: `NL-XXXXX`, `US-XXXXX`, etc.)
- Wikidata IDs (format: `Q12345`)
- VIAF IDs (format: numeric)
- URLs to institutional websites
- KvK numbers (Dutch: 8-digit format)
**Patterns**:
```regex
ISIL: [A-Z]{2}-[A-Za-z0-9]+
Wikidata: Q[0-9]+
VIAF: viaf.org/viaf/[0-9]+
KvK: [0-9]{8}
```
**Example**:
```markdown
Input: "ISIL code NL-AsdAM for Amsterdam Museum"
Output:
- identifier_scheme: "ISIL" # Identifier.identifier_scheme
- identifier_value: "NL-AsdAM" # Identifier.identifier_value
- institution_name: "Amsterdam Museum" # HeritageCustodian.name (for linking)
```
### Task 4: Relationship Extraction
**Objective**: Extract relationships between institutions.
**Schema Mapping**: Maps to `ChangeEvent` class from `schemas/provenance.yaml` (for mergers, splits) and future relationship modeling
**Relationship Types**:
- Parent-child (e.g., "X is part of Y")
- Partnerships (e.g., "X collaborates with Y")
- Network memberships (e.g., "X is a member of Z consortium")
- Merged organizations (e.g., "X merged with Y") → `ChangeTypeEnum.MERGER`
**Indicators**:
- "part of", "branch of", "division of"
- "in partnership with", "collaborates with"
- "member of", "belongs to"
- "merged with", "absorbed by" → Use `ChangeEvent` from `schemas/provenance.yaml`
### Task 5: Collection Metadata Extraction
**Objective**: Extract information about collections held by institutions.
**Schema Mapping**: Populates `Collection` class from `schemas/collections.yaml`
**Extract**:
- Collection names → `Collection.collection_name`
- Collection types (archival, bibliographic, museum objects)
- Subject areas → `Collection.subject_areas`
- Time periods covered → `Collection.temporal_coverage`
- Item counts (when mentioned) → `Collection.extent`
- Access information → `Collection.access_rights`
**Example**:
```markdown
Input: "The archive holds 15,000 documents from the 18th-19th centuries..."
Output:
- collection_type: "archival" # Collection metadata
- item_count: 15000 # Collection.extent
- time_period_start: "1700-01-01" # Collection.temporal_coverage
- time_period_end: "1899-12-31"
```
### Task 6: Digital Platform Identification
**Objective**: Identify digital platforms and systems used by institutions.
**Schema Mapping**: Populates `DigitalPlatform` class from `schemas/core.yaml`
**Platform Types**:
- Collection management systems (Atlantis, MAIS, CollectiveAccess, etc.)
- Digital repositories (DSpace, EPrints, Fedora)
- Discovery portals
- SPARQL endpoints
- APIs
**Extract**:
- Platform name → `DigitalPlatform.platform_name`
- Platform URL → `DigitalPlatform.platform_url`
- Metadata standards used → `DigitalPlatform.metadata_standards`
- Integration with aggregators (Europeana, DPLA, etc.)
### Task 7: Metadata Standards Detection
**Objective**: Identify which metadata standards institutions use.
**Schema Mapping**: Stores in `DigitalPlatform.metadata_standards` (list of strings)
**Standards to Detect**:
- Dublin Core
- MARC21
- EAD (Encoded Archival Description)
- BIBFRAME
- LIDO
- CIDOC-CRM
- Schema.org
- RiC-O (Records in Contexts)
- MODS, PREMIS, SPECTRUM, DACS
**Indicators**:
- Explicit mentions: "uses Dublin Core", "MARC21 records"
- Implicit: technical discussions about cataloging practices
### Task 8: Organizational Change Event Extraction (NEW - v0.2.0)
**Objective**: Extract significant organizational change events from conversation history.
**Schema Mapping**: Populates `ChangeEvent` class from `schemas/provenance.yaml`
**Change Types to Detect** (from `ChangeTypeEnum` in `schemas/enums.yaml`):
- **FOUNDING**: "established", "founded", "created", "opened"
- **CLOSURE**: "closed", "dissolved", "ceased operations", "shut down"
- **MERGER**: "merged with", "combined with", "joined with", "absorbed"
- **SPLIT**: "split into", "divided into", "separated from", "spun off"
- **ACQUISITION**: "acquired", "took over", "purchased"
- **RELOCATION**: "moved to", "relocated to", "transferred to"
- **NAME_CHANGE**: "renamed to", "formerly known as", "changed name to"
- **TYPE_CHANGE**: "became a museum", "converted to archive", "now operates as"
- **STATUS_CHANGE**: "reopened", "temporarily closed", "suspended operations"
- **RESTRUCTURING**: "reorganized", "restructured", "reformed"
- **LEGAL_CHANGE**: "incorporated as", "became a foundation", "legal status changed"
**Extract for Each Event**:
```yaml
change_history: # HeritageCustodian.change_history (list of ChangeEvent)
- event_id: "https://w3id.org/heritage/custodian/event/unique-id" # ChangeEvent.event_id
change_type: MERGER # ChangeEvent.change_type (ChangeTypeEnum from schemas/enums.yaml)
event_date: "2001-01-01" # ChangeEvent.event_date
event_description: >- # ChangeEvent.event_description
Merger of Institution A and Institution B to form new organization C.
Detailed description from conversation.
affected_organization: null # ChangeEvent.affected_organization (optional)
resulting_organization: null # ChangeEvent.resulting_organization (optional)
related_organizations: [] # ChangeEvent.related_organizations (optional)
source_documentation: "https://..." # ChangeEvent.source_documentation (optional)
```
**Temporal Context Indicators**:
- "In 2001, the museum merged with..."
- "After the renovation in 1985..."
- "Following the name change in 1968..."
- "The archive was relocated from X to Y in 1923"
**PROV-O Integration**:
- Map to `prov:Activity` in RDF serialization
- Link with `prov:wasInfluencedBy` from `HeritageCustodian`
- Use `prov:atTime` for event timestamps
- Track `prov:entity` (affected) and `prov:generated` (resulting) organizations
**Example Extraction**:
```markdown
Input: "The Noord-Hollands Archief was formed in 2001 through a merger of
Gemeentearchief Haarlem (founded 1910) and Rijksarchief in Noord-Holland
(founded 1802). The merger created a unified regional archive serving both
the city and province."
Output:
- event_id: "https://w3id.org/heritage/custodian/event/nha-merger-2001"
- change_type: MERGER # ChangeTypeEnum.MERGER
- event_date: "2001-01-01"
- event_description: "Merger of Gemeentearchief Haarlem (municipal archive, founded
1910) and Rijksarchief in Noord-Holland (state archive, founded
1802) to form Noord-Hollands Archief."
- confidence_score: 0.95 # From Provenance metadata
```
**GHCID Impact**:
- When institutions merge, relocate, or change names, GHCID may change
- Track old GHCID in `ghcid_history` with `valid_to` timestamp matching event date → `GHCIDHistoryEntry` from `schemas/provenance.yaml`
- Create new `GHCIDHistoryEntry` with `valid_from` matching event date
- Link change event to GHCID change via temporal correlation
**Indicators**:
### Task 9: Holy Sites Heritage Collection Identification
**Objective**: Identify religious sites that function as heritage custodians by maintaining cultural collections.
**Schema Mapping**: Populates `HeritageCustodian` class with `institution_type: HOLY_SITES`
**When to Classify as HOLY_SITES**:
Religious institutions qualify as HOLY_SITES heritage custodians when they manage:
- **Archival collections**: Historical documents, parish registers, ecclesiastical records
- **Library collections**: Rare manuscripts, theological texts, historical books
- **Museum collections**: Religious artifacts, liturgical objects, art collections
- **Cultural heritage**: Historical buildings with guided tours, preservation programs
**Patterns to Look For**:
- Church archives (parish records, baptismal registers, historical documents)
- Monastery libraries (manuscript collections, rare books)
- Cathedral treasuries (liturgical objects, religious art)
- Temple museums (Buddhist artifacts, historical collections)
- Mosque libraries (Islamic manuscripts, Quranic texts)
- Synagogue archives (Jewish community records, Torah scrolls)
- Abbey collections (medieval manuscripts, historical artifacts)
**Keywords and Indicators**:
- "church archive", "parish records", "ecclesiastical archive"
- "monastery library", "monastic collection", "scriptorium"
- "cathedral treasury", "cathedral museum"
- "temple library", "temple collection"
- "mosque library", "Islamic manuscript collection"
- "synagogue archive", "Jewish heritage collection"
- "religious heritage site", "pilgrimage site with museum"
**NOT Holy Sites** (use other types):
- Secular museums about religion (use MUSEUM)
- Academic religious studies centers (use RESEARCH_CENTER or UNIVERSITY)
- Government archives of church records (use ARCHIVE)
- Religious organizations without heritage collections (not heritage custodians)
**Example Extraction**:
```yaml
Input: "The Vatican Apostolic Archive holds over 85 km of shelving with
documents dating back to the 8th century, including papal bulls,
correspondence, and medieval manuscripts."
Output:
- name: Vatican Apostolic Archive
institution_type: HOLY_SITES # Religious institution managing heritage collections
description: >-
The Vatican Apostolic Archive (formerly Vatican Secret Archives) is
the central repository for papal and Vatican documents, holding over
35,000 volumes of historical records spanning 12 centuries.
locations:
- city: Vatican City
country: VA
collections:
- collection_name: Papal Documents
collection_type: archival
temporal_coverage: "0800-01-01/2024-12-31"
extent: "85 kilometers of shelving, 35,000+ volumes"
provenance:
data_source: CONVERSATION_NLP
confidence_score: 0.95
```
**Schema.org Mapping**:
- HOLY_SITES maps to `schema:PlaceOfWorship` in RDF serialization
- Can also use `schema:ArchiveOrganization` or `schema:Library` for collection-specific context
- Use multiple type assertions when appropriate
**Cross-Cultural Considerations**:
- Christianity: churches, cathedrals, monasteries, abbeys, convents
- Islam: mosques, madrasas (with historical libraries)
- Judaism: synagogues, yeshivas (with archival collections)
- Buddhism: temples, monasteries, pagodas (with artifact collections)
- Hinduism: temples (with historical collections)
- Sikhism: gurdwaras (with historical manuscripts)
- Other faiths: shrines, pilgrimage sites with documented heritage collections
## Data Quality and Provenance
### Provenance Tracking
**Every extracted record MUST include**:
```yaml
provenance:
data_source: CONVERSATION_NLP
data_tier: TIER_4_INFERRED
extraction_date: "2025-11-05T..."
extraction_method: "Subagent NER + pattern matching"
confidence_score: 0.85
conversation_id: "conversation-uuid"
source_url: null
verified_date: null
verified_by: null
```
### Confidence Scoring
Assign confidence scores (0.0-1.0) based on:
- **0.9-1.0**: Explicit, unambiguous mentions with context
- **0.7-0.9**: Clear mentions with some ambiguity
- **0.5-0.7**: Inferred from context, may need verification
- **0.3-0.5**: Low confidence, likely needs verification
- **0.0-0.3**: Very uncertain, flag for manual review
### Data Tier Assignment
- **TIER_1_AUTHORITATIVE**: CSV registries (ISIL, Dutch orgs)
- **TIER_2_VERIFIED**: Data from institutional websites (crawl4ai)
- **TIER_3_CROWD_SOURCED**: Wikidata, OpenStreetMap
- **TIER_4_INFERRED**: NLP-extracted from conversations
## Integration with CSV Data
### Cross-linking Strategy
1. **ISIL Code Matching** (primary)
- If conversation mentions ISIL code, link to CSV record
- High confidence match
2. **Name Matching** (secondary)
- Normalize names (lowercase, remove punctuation, handle abbreviations)
- Fuzzy matching with threshold > 0.85
- Check for alternative names
3. **Location + Type Matching** (tertiary)
- Match by city + institution type
- Lower confidence, requires manual verification
### Conflict Resolution
When conversation data conflicts with CSV data:
- **CSV data takes precedence** (higher tier)
- Mark conversation data with `verified: false`
- Note conflict in provenance metadata
- Create separate record if institutions are genuinely different
## NLP Models and Tools
### Recommended Approach: Agent-Based NER
**IMPORTANT**: Instead of directly using spaCy or other NER libraries in the main codebase, use **coding subagents** via the Task tool to conduct Named Entity Recognition and text extraction.
**Why Subagents**:
- Keeps the main codebase clean and maintainable
- Allows flexible experimentation with different NER approaches
- Subagents can choose the best tool for each specific extraction task
- Better separation of concerns: extraction logic vs. data pipeline
**How to Use Subagents for NER**:
1. Use the Task tool with `subagent_type="general"` for NER tasks
2. Provide clear prompts describing what entities to extract
3. Subagent will autonomously choose and apply appropriate NER tools (spaCy, transformers, regex, etc.)
4. Subagent returns structured extraction results
5. Main code validates and processes the results
## CRITICAL: Creating LinkML Instance Files
### Agent Capabilities Go Beyond Traditional NER
**IMPORTANT**: AI extraction agents are NOT limited to simple Named Entity Recognition. Unlike traditional NER tools that only identify entity boundaries and types, AI agents have **comprehensive understanding** and can:
1. **Extract Complete Records**: Capture ALL relevant information for each institution in one pass
2. **Infer Missing Data**: Use context to fill in fields that aren't explicitly stated
3. **Cross-Reference Within Documents**: Link related entities (locations, identifiers, events) automatically
4. **Maintain Consistency**: Ensure all extracted data conforms to the LinkML schema
5. **Generate Rich Metadata**: Create complete provenance tracking and confidence scores
### Mandatory: Create Complete LinkML Instance Files
When extracting data from conversations or other sources, agents MUST:
**✅ DO THIS**: Create complete LinkML-compliant YAML instance files with ALL available information
```yaml
# Example: data/instances/brazil_museums_001.yaml
---
# From schemas/core.yaml - HeritageCustodian class
- id: https://w3id.org/heritage/custodian/br/bnb-001
name: Biblioteca Nacional do Brasil
institution_type: LIBRARY # From schemas/enums.yaml
alternative_names:
- National Library of Brazil
- BNB
description: >-
The National Library of Brazil, located in Rio de Janeiro, is the largest
library in Latin America with over 9 million items. Founded in 1810 by
King João VI of Portugal. Collections include rare manuscripts, maps,
photographs, and Brazilian historical documents.
locations: # From schemas/core.yaml - Location class
- city: Rio de Janeiro
street_address: Avenida Rio Branco, 219
postal_code: "20040-008"
region: Rio de Janeiro
country: BR
# Note: lat/lon can be geocoded later if not in text
identifiers: # From schemas/core.yaml - Identifier class
- identifier_scheme: ISIL
identifier_value: BR-RjBN
identifier_url: https://isil.org/BR-RjBN
- identifier_scheme: VIAF
identifier_value: "123556639"
identifier_url: https://viaf.org/viaf/123556639
- identifier_scheme: Wikidata
identifier_value: Q1526131
identifier_url: https://www.wikidata.org/wiki/Q1526131
- identifier_scheme: Website
identifier_value: https://www.bn.gov.br
identifier_url: https://www.bn.gov.br
digital_platforms: # From schemas/core.yaml - DigitalPlatform class
- platform_name: Digital Library of the National Library of Brazil
platform_url: https://bndigital.bn.gov.br
platform_type: DISCOVERY_PORTAL
metadata_standards:
- Dublin Core
- MARC21
collections: # From schemas/collections.yaml - Collection class
- collection_name: Brazilian Historical Documents
collection_type: archival
subject_areas:
- Brazilian History
- Colonial Period
- Imperial Brazil
temporal_coverage: "1500-01-01/1889-11-15"
extent: "Approximately 2.5 million documents"
change_history: # From schemas/provenance.yaml - ChangeEvent class
- event_id: https://w3id.org/heritage/custodian/event/bnb-founding-1810
change_type: FOUNDING
event_date: "1810-01-01"
event_description: >-
Founded by King João VI of Portugal as the Royal Library
(Biblioteca Real) when the Portuguese court relocated to Brazil.
source_documentation: https://www.bn.gov.br/sobre-bn/historia
provenance: # From schemas/provenance.yaml - Provenance class
data_source: CONVERSATION_NLP
data_tier: TIER_4_INFERRED
extraction_date: "2025-11-05T14:30:00Z"
extraction_method: "AI agent comprehensive extraction from Brazilian GLAM conversation"
confidence_score: 0.92
conversation_id: "2025-09-22T14-40-15-0102c00a-4c0a-4488-bdca-5dd9fb94c9c5"
notes: >-
Extracted from conversation about Brazilian GLAM institutions.
Historical founding information cross-referenced from institutional website.
```
**❌ DO NOT DO THIS**: Return minimal JSON with only name and type
```json
// BAD - This is insufficient!
{
"name": "Biblioteca Nacional do Brasil",
"institution_type": "LIBRARY"
}
```
### Extraction Workflow for Agents
When processing a conversation or document:
1. **Read Entire Document First**: Don't extract piecemeal - understand the full context
2. **Identify ALL Entities**: Find every institution, location, identifier, event mentioned
3. **Gather Complete Information**: For each institution, extract:
- Basic metadata (name, type, description)
- All locations mentioned (even if just city/country)
- All identifiers (ISIL, Wikidata, VIAF, URLs)
- Digital platforms and systems
- Collection information
- Historical events (founding, mergers, relocations)
- Relationships to other institutions
4. **Create LinkML YAML**: Write a complete instance file with ALL extracted data
5. **Add Provenance**: Always include extraction metadata with confidence scores
6. **Validate**: Ensure output conforms to schema (use `linkml-validate` if available)
### Example Agent Prompt for Comprehensive Extraction
```
Extract ALL heritage institutions from the following conversation about Brazilian GLAM institutions.
For EACH institution found, create a COMPLETE LinkML-compliant record including:
- Institution name, type, and description
- ALL locations mentioned (cities, addresses, regions)
- ALL identifiers (ISIL codes, Wikidata IDs, VIAF IDs, URLs)
- Digital platforms, systems, or websites
- Collection information (types, subjects, time periods, extent)
- Historical events (founding dates, mergers, relocations, name changes)
- Relationships to other organizations
Output: YAML file conforming to schemas/core.yaml, schemas/enums.yaml,
schemas/provenance.yaml, and schemas/collections.yaml
Use your understanding to:
- Infer missing fields from context (e.g., country from city names)
- Consolidate information scattered across multiple conversation turns
- Create rich descriptions summarizing key facts
- Assign appropriate confidence scores based on explicitness of mentions
Remember: You are NOT a simple NER tool. Use your full comprehension abilities
to create the most complete, accurate, and useful records possible.
```
### Multiple Institutions Per File
When a conversation discusses many institutions, create ONE YAML file with a list:
```yaml
---
# data/instances/netherlands_limburg_museums.yaml
- id: https://w3id.org/heritage/custodian/nl/bonnefantenmuseum
name: Bonnefantenmuseum
institution_type: MUSEUM
# ... complete record ...
- id: https://w3id.org/heritage/custodian/nl/thermenmuseum
name: Thermenmuseum
institution_type: MUSEUM
# ... complete record ...
- id: https://w3id.org/heritage/custodian/nl/limburgs-museum
name: Limburgs Museum
institution_type: MUSEUM
# ... complete record ...
```
### Field Completion Strategies
Even when information is incomplete, do your best:
- **No explicit institution type?** Infer from context ("national library" → LIBRARY)
- **Only city mentioned?** That's fine - add `locations: [{city: "Amsterdam", country: "NL"}]`
- **No ISIL code?** Check if you can infer the format (NL-CityCode) or leave it out
- **No description?** Create one from available facts
- **Uncertain data?** Lower the confidence score but still include it
### Validation and Quality Control
After creating instance files:
1. **Schema Validation**: If possible, run `linkml-validate -s schemas/heritage_custodian.yaml data/instances/your_file.yaml`
2. **Completeness Check**: Ensure every institution has at minimum:
- `id` (generate from country + institution name slug)
- `name`
- `institution_type`
- `provenance` (with data_source, extraction_date, confidence_score)
3. **Consistency Check**: Same institution mentioned multiple times? Merge into one record
4. **Quality Flags**: If confidence < 0.5, add note in `provenance.notes` explaining uncertainty
### Extraction Stack (for Subagents)
When subagents perform extraction, they may use:
1. **Pattern matching** for identifiers (primary approach)
- Regex for ISIL, VIAF, Wikidata IDs
- URL extraction and normalization
- High precision, no dependencies
2. **NER libraries** (via subagents only)
- spaCy: `en_core_web_trf`, `nl_core_news_lg`, `xx_ent_wiki_sm`
- Transformers for classification
- Used by subagents, not directly in main code
3. **Fuzzy matching** for deduplication
- `rapidfuzz` library
- Levenshtein distance for name matching
### Processing Pipeline
```
Conversation JSON
↓
Parse & Extract Text
↓
[SUBAGENT] NER Extraction
- Subagent uses spaCy/transformers/patterns
- Returns structured entities
↓
Pattern Matching (identifiers, URLs)
↓
Classification (institution type, standards)
↓
Geocoding (locations)
↓
Cross-link with CSV (ISIL/name matching)
↓
LinkML Validation
↓
Export (RDF, JSON-LD, CSV, Parquet)
```
## Agent Interaction Patterns
### When Asked to Extract Data from Conversations
1. **Start Small**: Begin with 1-2 conversation files to test extraction logic
2. **Show Examples**: Display extracted entities with confidence scores
3. **Ask for Validation**: Show uncertain extractions for user confirmation
4. **Iterate**: Refine patterns based on feedback
5. **Batch Process**: Once patterns are validated, process all 139 files
### When Asked to Design NLP Components
1. **Reference Schema**: Always refer to the modular schema v0.2.1:
- Core classes: `schemas/core.yaml` (HeritageCustodian, Location, Identifier, etc.)
- Enumerations: `schemas/enums.yaml` (InstitutionTypeEnum, ChangeTypeEnum, etc.)
- Provenance: `schemas/provenance.yaml` (Provenance, ChangeEvent, etc.)
- See schema overview in the "Schema Reference (v0.2.1)" section above
2. **Consult Base Ontologies**: BEFORE designing extraction logic, review relevant ontologies:
- **Dutch institutions**: Study TOOI ontology (`/data/ontology/tooiont.ttl`)
- **EU/global institutions**: Study CPOV ontology (`/data/ontology/core-public-organisation-ap.ttl`)
- **All institutions**: Reference Schema.org patterns (`/data/ontology/schemaorg.owl`)
- See "Base Ontologies for Global GLAM Data" section above for decision tree
3. **Use Design Patterns**: Follow patterns in `docs/plan/global_glam/05-design-patterns.md`
4. **Track Provenance**: Every extraction must include provenance metadata (from `schemas/provenance.yaml`)
5. **Handle Multilingual**: Conversations cover 60+ countries, expect multilingual content
6. **Error Handling**: Use Result pattern, never fail silently
### When Asked to Validate Data
1. **LinkML Validation**: Use `linkml-validate` to check schema compliance
2. **Cross-reference**: Compare with CSV data when applicable
3. **Check Identifiers**: Validate ISIL format, check Wikidata exists
4. **Geographic Verification**: Geocode addresses, verify country codes
5. **Duplicate Detection**: Use fuzzy matching to find potential duplicates
## Example Agent Workflows
### Workflow 1: Extract Brazilian Institutions
```bash
# User request
"Extract all museum, library, and archive names from the Brazilian GLAM conversation"
# Agent actions
1. Read conversation: 2025-09-22T14-40-15-0102c00a-4c0a-4488-bdca-5dd9fb94c9c5-Brazilian_GLAM_collection_inventories.json
2. Parse chat_messages array
3. **Launch subagent** to extract institutions using NER
- Subagent analyzes text and extracts ORG entities
- Filters for heritage-related keywords
- Classifies institution types
- Returns structured results
4. Extract locations (cities in Brazil)
5. Geocode using Nominatim
6. Create HeritageCustodian records
7. Add provenance metadata (data_source: CONVERSATION_NLP, extraction_method: "Subagent NER")
8. Validate with LinkML schema
9. Export to JSON-LD
10. Report results with confidence scores
```
### Workflow 2: Cross-link Dutch Institutions
```bash
# User request
"Cross-link the Dutch organizations CSV with any Dutch institutions found in conversations"
# Agent actions
1. Load data/voorbeeld_lijst_organisaties_en_diensten-totaallijst_nederland.csv
2. Parse into DutchHeritageCustodian records
3. Extract all NL-* ISIL codes
4. Search all conversation files for mentions of these ISIL codes
5. Fuzzy match organization names
6. For matches:
- Merge metadata
- Mark CSV data as TIER_1
- Mark conversation data as TIER_4
- Resolve conflicts (CSV wins)
7. For Dutch institutions in conversations NOT in CSV:
- Create new records
- Mark as TIER_4
- Flag for verification
8. Export merged dataset
```
### Workflow 3: Build Global Institution Map
```bash
# User request
"Create a geographic distribution map of all extracted institutions"
# Agent actions
1. Process all 139 conversation files
2. **Launch subagent(s)** to extract institution names + locations from each file
3. Geocode all addresses
4. Group by country
5. Count institutions per country
6. Generate GeoJSON for mapping
7. Create visualization (Leaflet, Mapbox, etc.)
8. Export statistics:
- Institutions per country
- Institutions per type
- Geographic coverage
- Data quality (tier distribution)
```
## Multi-language Considerations
### Language Detection
- Detect language of conversation content
- Subagents will choose appropriate NER models per language
- Multilingual support handled by subagents
### Common Languages in Dataset
- English (international institutions)
- Dutch (Netherlands institutions)
- Portuguese (Brazil)
- Spanish (Latin America, Spain)
- Vietnamese, Japanese, Thai, Korean, Arabic, Russian, etc.
### Translation Strategy
- DO NOT translate institution names (preserve original)
- Optionally translate descriptions for searchability
- Store language tags with text fields
- Use multilingual identifiers (Wikidata) for linking
## Output Formats
### Primary Output: JSON-LD
Linked Data format for semantic web integration:
```jsonld
{
"@context": "https://w3id.org/heritage/custodian/context.jsonld",
"@type": "HeritageCustodian",
"@id": "https://example.org/institution/123",
"name": "Amsterdam Museum",
"institution_type": "MUSEUM",
...
}
```
### Secondary Outputs
- **RDF/Turtle**: For SPARQL querying
- **CSV**: For spreadsheet analysis
- **Parquet**: For data warehousing
- **SQLite**: For local querying
## Testing and Validation
### Unit Tests
Test extraction functions with known inputs:
```python
def test_extract_isil_codes():
text = "The ISIL code NL-AsdAM identifies Amsterdam Museum"
codes = extract_isil_codes(text)
assert codes == [{"scheme": "ISIL", "value": "NL-AsdAM"}]
```
### Integration Tests
Test full pipeline with sample conversations:
```python
def test_brazilian_museum_extraction():
conversation = load_json("Brazilian_GLAM_collection_inventories.json")
records = extract_heritage_custodians(conversation)
assert len(records) > 0
assert all(r.provenance.data_source == "CONVERSATION_NLP" for r in records)
```
### Validation Tests
Ensure LinkML schema compliance:
```python
def test_linkml_validation():
record = create_heritage_custodian(...)
validator = SchemaValidator(schema="heritage_custodian.yaml")
result = validator.validate(record)
assert result.is_valid
```
## Performance Optimization
### Batch Processing
- Process conversations in parallel (multiprocessing)
- Cache geocoding results (15-minute TTL)
- Deduplicate entity extraction
### Incremental Updates
- Track last processed timestamp
- Only process new/updated conversations
- Maintain state in SQLite database
### Resource Management
- Limit concurrent API calls (Nominatim: 1 req/sec)
- Use connection pooling for HTTP requests
- Stream large JSON files instead of loading into memory
## Error Handling
### Common Errors and Solutions
1. **JSON Parsing Errors**
- Malformed JSON files
- Solution: Validate JSON schema, report file path
2. **NER Model Errors**
- Missing spaCy model
- Solution: Provide installation instructions, download automatically
3. **Geocoding Failures**
- Unknown location, rate limit exceeded
- Solution: Cache results, implement backoff, mark as unverified
4. **LinkML Validation Failures**
- Required field missing, invalid enum value
- Solution: Log validation errors, provide field mapping
5. **Encoding Issues**
- Non-UTF-8 characters
- Solution: Use UTF-8 everywhere, handle decode errors gracefully
## Schema Quirks and Implementation Notes
**IMPORTANT**: These are critical implementation details discovered during development. Read carefully to avoid bugs.
### Provenance Model Quirks
The `Provenance` model does **NOT** have a `notes` field:
```python
# ❌ WRONG - Provenance has no 'notes' field
provenance = Provenance(
data_source=DataSource.CSV_REGISTRY,
notes="Some observation" # This will fail!
)
# ✅ CORRECT - Use HeritageCustodian.description instead
custodian = HeritageCustodian(
name="Museum Name",
description="Notes and remarks go here", # Put notes here
provenance=Provenance(...)
)
```
### Field Naming Conventions
Always use the correct field names (check the schema when in doubt):
```python
# ❌ WRONG
custodian.institution_types # Plural, list
custodian.location # Singular
# ✅ CORRECT
custodian.institution_type # Singular, single enum value
custodian.locations # Plural, always a list (even with one item)
```
### Pydantic v1 Enum Behavior
This project uses Pydantic v1. Enum fields are **already strings**, not enum objects:
```python
# ❌ WRONG - Don't use .value accessor
print(custodian.institution_type.value) # AttributeError!
# ✅ CORRECT - Enum fields are already strings
print(custodian.institution_type) # "MUSEUM", "ARCHIVE", etc.
# Same for platform types
platform.platform_type # Already a string, not an enum object
```
### Required vs. Optional Fields
Many fields are optional but have validation rules. Always check for `None`:
```python
# Optional fields that may be None
custodian.locations # Optional[List[Location]]
custodian.identifiers # Optional[List[Identifier]]
custodian.digital_platforms # Optional[List[DigitalPlatform]]
custodian.description # Optional[str]
# Always check before iterating
if custodian.locations:
for location in custodian.locations:
print(location.city)
```
### CSV Parsing Best Practices
1. **Handle UTF-8 BOM**: Use `encoding='utf-8-sig'` when reading CSVs
2. **Normalize headers**: Strip whitespace, handle multiline headers
3. **Warn on errors**: Skip invalid rows but log warnings
4. **Preserve originals**: Store raw CSV data in intermediate models before conversion
Example:
```python
with open(csv_path, 'r', encoding='utf-8-sig') as f:
reader = csv.DictReader(f)
for row in reader:
try:
record = parse_row(row)
except ValidationError as e:
print(f"Warning: Skipping row {row}: {e}")
continue
```
### Date Handling
Dates may be in various formats or empty:
```python
# Handle empty dates
date_str = row.get('toegekend_op', '').strip()
assigned_date = datetime.fromisoformat(date_str) if date_str else None
# Provenance extraction_date is required (use current time)
from datetime import datetime, timezone
extraction_date = datetime.now(timezone.utc)
```
### Testing Strategies
1. **Unit tests**: Test model validation with known inputs
2. **Integration tests**: Test full file parsing with fixtures
3. **Edge case tests**: Empty files, malformed rows, minimal data
4. **Real data tests**: Always validate with actual CSV files
Fixture scope matters:
```python
# ❌ WRONG - Class-scoped fixture not available to other classes
class TestFoo:
@pytest.fixture
def sample_file(self):
...
# ✅ CORRECT - Module-scoped fixture available to all test classes
@pytest.fixture
def sample_file(): # At module level, not in a class
...
```
## Next Steps for Agents
When continuing this project, agents should:
1. **Implement Parser Module** (`src/glam_extractor/parsers/`) ✅ **COMPLETE**
- ✅ ISIL registry parser (10 tests, 84% coverage)
- ✅ Dutch organizations parser (18 tests, 98% coverage)
- ⏳ Conversation JSON parser (next priority)
2. **Implement Extractor Module** (`src/glam_extractor/extractors/`)
- spaCy NER integration
- Pattern-based identifier extraction
- Institution type classifier
- Relationship extractor
3. **Implement Geocoder Module** (`src/glam_extractor/geocoding/`)
- Nominatim client with caching
- GeoNames integration
- Coordinate validation
4. **Implement Validator Module** (`src/glam_extractor/validators/`)
- LinkML schema validator
- Cross-reference validator (CSV vs. conversation)
- Duplicate detector
5. **Implement Exporter Module** (`src/glam_extractor/exporters/`)
- JSON-LD exporter
- RDF/Turtle exporter
- CSV exporter
- Parquet exporter
- SQLite database builder
6. **Create Test Fixtures** (`tests/fixtures/`)
- Sample conversation JSONs
- Expected extraction outputs
- Validation test cases
7. **Document Agent Prompts** (`docs/agent-prompts/`)
- Reusable prompts for common extraction tasks
- Few-shot examples for LLM-based extraction
- Quality review checklists
## Persistent Identifiers (GHCID)
**🚨 COLLISION RESOLUTION: NATIVE LANGUAGE NAME SUFFIX 🚨**
When multiple institutions generate the same base GHCID, collisions are resolved by appending the **full legal name in native language in snake_case format**.
**Collision Suffix Rules**:
- ✅ Use the institution's full official name in its native language
- ✅ Convert to snake_case (lowercase, underscores for spaces)
- ✅ Remove apostrophes, accents, commas, and other punctuation/diacritics
- ✅ Only add suffix on collision (not by default)
- ✅ First-added institution keeps base GHCID; later additions get name suffix
**Examples**:
- Base GHCID collision: `NL-NH-AMS-M-SM` (two museums with "SM" abbreviation)
- First institution: `NL-NH-AMS-M-SM` (Stedelijk Museum, added first - no suffix)
- Second institution: `NL-NH-AMS-M-SM-science_museum_amsterdam` (added later - gets suffix)
**Name Normalization**:
```
"Musée d'Orsay" → "musee_dorsay"
"Biblioteca Nacional do Brasil" → "biblioteca_nacional_do_brasil"
"北京故宫博物院" → "beijing_gugong_bowuyuan" (pinyin transliteration)
"Österreichische Nationalbibliothek" → "osterreichische_nationalbibliothek"
```
**Note**: The GHCID string (including any name suffix) gets hashed to UUID, so the longer name won't be visible to end users - they see only the UUID.
---
### 🚨 SETTLEMENT STANDARDIZATION: GEONAMES IS AUTHORITATIVE 🚨
**ALL settlement names in GHCID MUST be derived from GeoNames, not from source data.**
The GeoNames geographical database (`/data/reference/geonames.db`) is the **single source of truth** for:
- Settlement names (cities, towns, villages)
- Settlement 3-letter codes
- Administrative region codes (admin1 → ISO 3166-2)
**Why GeoNames?**
- **Consistency**: Same coordinates → same settlement → same GHCID component
- **Disambiguation**: Handles duplicate settlement names across regions
- **Standardization**: Provides ASCII-safe names for identifiers
- **Persistence**: Geographic reality is stable, ensuring GHCID stability
**Settlement Resolution Process**:
1. **Coordinates Available (Preferred)**: Use reverse geocoding to find nearest GeoNames settlement
2. **Name Only (Fallback)**: Look up settlement name in GeoNames with fuzzy matching
3. **Manual (Last Resort)**: Flag entry with `settlement_code: XXX` for review
### 🚨 CRITICAL: GeoNames Feature Code Filtering 🚨
**NEVER use neighborhoods or districts (PPLX) for GHCID generation. ONLY use proper settlements (cities, towns, villages).**
GeoNames classifies populated places with feature codes. When reverse geocoding coordinates to find a settlement, you MUST filter by feature code to ensure you get a city/town/village, NOT a neighborhood or district.
**ALLOWED Feature Codes** (use these for GHCID settlements):
| Code | Description | Example |
|------|-------------|---------|
| **PPL** | Populated place (city/town/village) | Apeldoorn, Hamont, Lelystad |
| **PPLA** | Seat of first-order admin division | Provincial capitals |
| **PPLA2** | Seat of second-order admin division | Municipal seats |
| **PPLA3** | Seat of third-order admin division | District seats |
| **PPLA4** | Seat of fourth-order admin division | Sub-district seats |
| **PPLC** | Capital of a political entity | Amsterdam, Brussels |
| **PPLS** | Populated places (multiple) | Settlement clusters |
| **PPLG** | Seat of government | The Hague (when different from capital) |
**EXCLUDED Feature Codes** (NEVER use for GHCID):
| Code | Description | Why Excluded |
|------|-------------|--------------|
| **PPLX** | Section of populated place | Neighborhoods, districts, quarters (e.g., "Binnenstad", "Amsterdam Binnenstad") |
**Example of the Problem**:
```sql
-- BAD: Query without feature code filter returns neighborhoods
SELECT name, feature_code, population FROM cities
WHERE country_code='NL' ORDER BY distance LIMIT 1;
-- Result: "Binnenstad" (PPLX, pop 4,900) ❌ WRONG
-- GOOD: Query WITH feature code filter returns proper settlements
SELECT name, feature_code, population FROM cities
WHERE country_code='NL'
AND feature_code IN ('PPL', 'PPLA', 'PPLA2', 'PPLA3', 'PPLA4', 'PPLC', 'PPLS', 'PPLG')
ORDER BY distance LIMIT 1;
-- Result: "Apeldoorn" (PPL, pop 136,670) ✅ CORRECT
```
**Implementation in SQL**:
```sql
-- Correct reverse geocoding query with feature code filter
SELECT
name, ascii_name, admin1_code, admin1_name,
latitude, longitude, geonames_id, population, feature_code,
((latitude - ?) * (latitude - ?) + (longitude - ?) * (longitude - ?)) as distance_sq
FROM cities
WHERE country_code = ?
AND feature_code IN ('PPL', 'PPLA', 'PPLA2', 'PPLA3', 'PPLA4', 'PPLC', 'PPLS', 'PPLG')
ORDER BY distance_sq
LIMIT 1
```
**Verification**: Always check `feature_code` in location_resolution metadata:
```yaml
location_resolution:
method: REVERSE_GEOCODE
geonames_id: 2759706
geonames_name: Apeldoorn
feature_code: PPL # ← MUST be PPL, PPLA, PPLA2, PPLA3, PPLA4, PPLC, PPLS, or PPLG
admin1_code: '03'
region_code: GE
country_code: NL
```
**If you see `feature_code: PPLX`**, the GHCID is WRONG and must be regenerated.
### Country Code Detection for GeoNames Lookups
**CRITICAL**: Determine country code from entry data BEFORE calling GeoNames reverse geocoding.
GeoNames queries are country-specific. Using the wrong country code will return incorrect results or no results.
**Country Code Resolution Priority**:
1. `zcbs_enrichment.country` - Most explicit source
2. `location.country` - Direct location field
3. `locations[].country` - Array location field
4. `original_entry.country` - CSV source field
5. `google_maps_enrichment.address` - Parse country from address string (", Belgium", ", Germany")
6. `wikidata_enrichment.located_in.label` - Infer from Wikidata location
7. Default: `"NL"` (Netherlands) - Only if no other source available
**Example Country Detection Code**:
```python
# Determine country code FIRST
country_code = "NL" # Default
if entry.get('zcbs_enrichment', {}).get('country'):
country_code = entry['zcbs_enrichment']['country']
elif entry.get('location', {}).get('country'):
country_code = entry['location']['country']
elif entry.get('google_maps_enrichment', {}).get('address', ''):
address = entry['google_maps_enrichment']['address']
if ', Belgium' in address or ', België' in address:
country_code = "BE"
elif ', Germany' in address or ', Deutschland' in address:
country_code = "DE"
# THEN call reverse geocoding with correct country
result = reverse_geocode_to_city(latitude, longitude, country_code)
```
**GHCID Settlement Code Format**:
```
NL-{REGION}-{SETTLEMENT}-{TYPE}-{ABBREV}
^^^^^^^^^^^
3-letter code from GeoNames
```
**Code Generation Rules**:
- Single word: First 3 letters → `Amsterdam` = `AMS`, `Lelystad` = `LEL`
- Dutch article (`de`, `het`, `den`, `'s`): Article initial + 2 from main word → `Den Haag` = `DHA`
- Multi-word: Initials (up to 3) → `Nieuw Amsterdam` = `NAM`
**Historical Custodians - Measurement Point Rule**:
For heritage custodians that no longer exist or have historical locations:
- Use the **modern-day settlement** (as of 2025-12-01) where the coordinates fall
- GeoNames reflects current geographic reality
- Historical place names should NOT be used for GHCID generation
Example: A museum operating 1900-1950 in what is now Lelystad (before Flevoland existed) uses `LEL`, not historical names.
### 🚨 CRITICAL: XXX Placeholders Are TEMPORARY - Research Required 🚨
**XXX placeholders for region/settlement codes are NEVER acceptable as a final state. They indicate missing data that MUST be researched and resolved.**
When you encounter or generate entries with `XX` (unknown region) or `XXX` (unknown settlement):
**Step 1: Identify the Last Known Physical Location**
For **destroyed/historical institutions**:
- Use the **last recorded physical location** where the institution operated
- Example: Gaza Cultural Center destroyed in 2024 → use Gaza City coordinates (`PS-GZ-GAZ-M-GCC`)
For **refugee/diaspora organizations**:
- Use the location of their **current headquarters** OR **original founding location**
- Document which location type was used in `location_resolution.notes`
For **digital-only platforms**:
- Use the location of the **parent/founding organization**
- Example: Interactive Encyclopedia of Palestine Question → Institute for Palestine Studies → Beirut (`LB-BA-BEI-D-IEPQ`)
**Step 2: Research Sources (Priority Order)**
1. **Wikidata** - Search for the institution, check P131 (located in) or P159 (headquarters location)
2. **Google Maps** - Search institution name, extract coordinates
3. **Official Website** - Look for contact page, about page with address
4. **Web Archive** - Use archive.org for destroyed/closed institutions
5. **Academic Sources** - Papers, reports mentioning the institution
6. **News Articles** - Particularly useful for destroyed heritage sites
**Step 3: Update Entry with Resolved Location**
```yaml
# BEFORE (unacceptable)
ghcid:
ghcid_current: PS-XX-XXX-A-NAPR
location_resolution:
method: NAME_LOOKUP
country_code: PS
region_code: XX
city_code: XXX
# AFTER (properly researched)
ghcid:
ghcid_current: PS-GZ-GAZ-A-NAPR
location_resolution:
method: MANUAL_RESEARCH
country_code: PS
region_code: GZ
region_name: Gaza Strip
city_code: GAZ
city_name: Gaza City
geonames_id: 281133
research_date: "2025-12-06T00:00:00Z"
research_sources:
- type: wikidata
id: Q123456
claim: P131
- type: web_archive
url: https://web.archive.org/web/20231001/https://institution-website.org/contact
notes: "Located in Gaza City prior to destruction in 2024"
```
**Step 4: Rename File to Match New GHCID**
Files MUST be renamed when GHCID changes:
```bash
# Old file
data/custodian/PS-XX-XXX-A-NAPR.yaml
# New file after research
data/custodian/PS-GZ-GAZ-A-NAPR.yaml
```
**Common XXX Placeholder Scenarios and Solutions**:
| Scenario | Solution |
|----------|----------|
| Destroyed Gaza institution | Use pre-destruction coordinates (Gaza City, Khan Yunis, etc.) |
| Refugee archive (diaspora) | Use current headquarters OR founding camp location |
| Digital platform (online only) | Use parent organization headquarters |
| Decentralized initiative | Use founding location or primary organizer location |
| Historical institution (closed) | Use last operating location |
| Institution with country but no city | Research using name + country in Wikidata/Google |
**NEVER**:
- ❌ Leave XXX placeholders in production data
- ❌ Use "Online" or "Palestine" as location values
- ❌ Skip location research because it's "difficult"
- ❌ Use XX/XXX for diaspora organizations (they have real locations)
**ALWAYS**:
- ✅ Document research sources in `location_resolution.research_sources`
- ✅ Add notes explaining location choice for complex cases
- ✅ Update GHCID history when location is resolved
- ✅ Rename files to match corrected GHCID
**Netherlands Admin1 Code Mapping** (GeoNames → ISO 3166-2):
| GeoNames | Province | ISO Code |
|----------|----------|----------|
| 01 | Drenthe | DR |
| 02 | Friesland | FR |
| 03 | Gelderland | GE |
| 04 | Groningen | GR |
| 05 | Limburg | LI |
| 06 | Noord-Brabant | NB |
| 07 | Noord-Holland | NH |
| 09 | Utrecht | UT |
| 10 | Zeeland | ZE |
| 11 | Zuid-Holland | ZH |
| 15 | Overijssel | OV |
| 16 | Flevoland | FL |
**Provenance Tracking**: Record GeoNames resolution in entry metadata:
```yaml
location_resolution:
method: REVERSE_GEOCODE # or NAME_LOOKUP or MANUAL
geonames_id: 2751792
geonames_name: Lelystad
settlement_code: LEL
admin1_code: "16"
region_code: FL
resolution_date: "2025-12-01T00:00:00Z"
```
**See**: `.opencode/GEONAMES_SETTLEMENT_RULES.md` for complete documentation.
---
### 🚨 INSTITUTION ABBREVIATION: EMIC NAME FIRST-LETTER PROTOCOL 🚨
**The institution abbreviation component uses the FIRST LETTER of each significant word in the official emic (native language) name.**
**⚠️ GRANDFATHERING POLICY (PID STABILITY)**
Existing GHCIDs created before December 2025 are **grandfathered** - their abbreviations will NOT be updated even if derived from English translations rather than emic names. This preserves PID stability per the "Cool URIs Don't Change" principle.
**Applies to:**
- 817 UNESCO Memory of the World custodian files enriched with `custodian_name.emic_name`
- Abbreviations like `NLP` (National Library of Peru) remain unchanged even though emic name is "Biblioteca Nacional del Perú" (would be `BNP`)
**For NEW custodians only:** Apply emic name abbreviation protocol described below.
**Abbreviation Rules**:
1. Use the **CustodianName** (official emic name), NOT an English translation
2. Take the **first letter** of each word
3. **Skip prepositions, articles, and conjunctions** in all languages
4. **Skip digits and numeric tokens** (e.g., "40-45", "1945", "III")
5. Convert to **UPPERCASE**
6. Remove accents/diacritics (á→A, ñ→N, ö→O)
7. Maximum **10 characters**
**Skipped Words** (prepositions/articles/conjunctions by language):
- **Dutch**: de, het, een, van, voor, in, op, te, den, der, des, 's, aan, bij, met, naar, om, tot, uit, over, onder, door, en, of
- **English**: a, an, the, of, in, at, on, to, for, with, from, by, as, under, and, or, but
- **French**: le, la, les, un, une, des, de, d, du, à, au, aux, en, dans, sur, sous, pour, par, avec, l, et, ou
- **German**: der, die, das, den, dem, des, ein, eine, einer, einem, einen, von, zu, für, mit, bei, nach, aus, vor, über, unter, durch, und, oder
- **Spanish**: el, la, los, las, un, una, unos, unas, de, del, a, al, en, con, por, para, sobre, bajo, y, o, e, u
- **Portuguese**: o, a, os, as, um, uma, uns, umas, de, do, da, dos, das, em, no, na, nos, nas, para, por, com, sobre, sob, e, ou
- **Italian**: il, lo, la, i, gli, le, un, uno, una, di, del, dello, della, dei, degli, delle, a, al, allo, alla, ai, agli, alle, da, dal, dallo, dalla, dai, dagli, dalle, in, nel, nello, nella, nei, negli, nelle, su, sul, sullo, sulla, sui, sugli, sulle, con, per, tra, fra, e, ed, o, od
**TODO**: Expand to comprehensive global coverage for all ISO 639-1 languages as project expands.
**Examples**:
| Emic Name | Abbreviation | Explanation |
|-----------|--------------|-------------|
| Heemkundige Kring De Goede Stede | HKGS | Skip "De" |
| De Hollandse Cirkel | HC | Skip "De" |
| Historische Vereniging Nijeveen | HVN | All significant words |
| Rijksmuseum Amsterdam | RA | All significant words |
| Musée d'Orsay | MO | Skip "d'" (d = de) |
| Biblioteca Nacional do Brasil | BNB | Skip "do" |
| L'Académie française | AF | Skip "L'" |
| Museum van de Twintigste Eeuw | MTE | Skip "van", "de" |
| Koninklijke Bibliotheek van België | KBB | Skip "van" |
**GHCID Format with Abbreviation**:
```
NL-{REGION}-{SETTLEMENT}-{TYPE}-{ABBREV}
^^^^^^^^
First letter of each significant word in emic name
```
**Implementation**: See `src/glam_extractor/identifiers/ghcid.py:extract_abbreviation_from_name()`
### 🚨 CRITICAL: Special Characters MUST Be Excluded from Abbreviations 🚨
**When generating abbreviations for GHCID, special characters and symbols MUST be completely removed. Only alphabetic characters (A-Z) are permitted in the abbreviation component.**
**RATIONALE**:
1. **URL/URI safety** - Special characters require encoding in URIs
2. **Filename safety** - Characters like `&`, `/`, `\`, `:` are invalid in filenames
3. **Parsing consistency** - Avoids delimiter conflicts in data pipelines
4. **Cross-system compatibility** - Ensures interoperability with all systems
5. **Human readability** - Clean identifiers are easier to communicate
**CHARACTERS TO REMOVE** (exhaustive list):
- **Ampersand**: `&` (e.g., "Records & Archives" → "RA", NOT "R&A")
- **Slash**: `/` (e.g., "Art/Design Museum" → "ADM", NOT "A/DM")
- **Backslash**: `\`
- **Plus**: `+` (e.g., "Culture+" → "C")
- **At sign**: `@`
- **Hash/Pound**: `#`
- **Percent**: `%`
- **Dollar**: `$`
- **Asterisk**: `*`
- **Parentheses**: `( )`
- **Brackets**: `[ ] { }`
- **Pipe**: `|`
- **Colon**: `:`
- **Semicolon**: `;`
- **Quotation marks**: `" ' \``
- **Comma**: `,`
- **Period**: `.` (unless part of abbreviation like "U.S." → "US")
- **Hyphen**: `-` (skip, do not replace with letter)
- **Underscore**: `_`
- **Equals**: `=`
- **Question mark**: `?`
- **Exclamation**: `!`
- **Tilde**: `~`
- **Caret**: `^`
- **Less/Greater than**: `< >`
**EXAMPLES**:
| Source Name | Correct Abbreviation | Incorrect (WRONG) |
|-------------|---------------------|-------------------|
| Department of Records & Information Management | DRIM | DR&IM ❌ |
| Art + Culture Center | ACC | A+CC ❌ |
| Museum/Gallery Amsterdam | MGA | M/GA ❌ |
| Heritage@Digital | HD | H@D ❌ |
| Archives (Historical) | AH | A(H) ❌ |
| Research & Development Institute | RDI | R&DI ❌ |
**REAL-WORLD EXAMPLE** (from `data/custodian/SX-XX-PHI-O-DR&IMSM.yaml`):
```yaml
# INCORRECT (current file - needs correction):
ghcid_current: SX-XX-PHI-O-DR&IMSM # ❌ Contains "&"
# CORRECT (should be):
ghcid_current: SX-XX-PHI-O-DRIMSM # ✅ Alphabetic only
```
**Implementation**: When extracting first letters from words containing special characters:
1. Split the word on special characters: "Records&Information" → ["Records", "Information"]
2. Take first letter from each resulting segment: "R" + "I" = "RI"
3. Or skip the special character entirely and treat as one word if no space around it
**See**: `.opencode/ABBREVIATION_SPECIAL_CHAR_RULE.md` for complete documentation
### 🚨 CRITICAL: Diacritics MUST Be Normalized to ASCII in Abbreviations 🚨
**When generating abbreviations for GHCID, diacritics (accented characters) MUST be normalized to their ASCII base letter equivalents. Only ASCII uppercase letters (A-Z) are permitted.**
This rule applies to ALL languages with diacritical marks including Czech, Polish, German, French, Spanish, Portuguese, Nordic languages, Hungarian, Romanian, Turkish, and others.
**RATIONALE**:
1. **URI/URL safety** - Non-ASCII characters require percent-encoding
2. **Cross-system compatibility** - ASCII is universally supported
3. **Filename safety** - Some systems have issues with non-ASCII filenames
4. **Human readability** - Easier to type and communicate
**DIACRITICS NORMALIZATION TABLE**:
| Language | Diacritics | ASCII Equivalent |
|----------|------------|------------------|
| **Czech** | Č, Ř, Š, Ž, Ě, Ů | C, R, S, Z, E, U |
| **Polish** | Ł, Ń, Ó, Ś, Ź, Ż, Ą, Ę | L, N, O, S, Z, Z, A, E |
| **German** | Ä, Ö, Ü, ß | A, O, U, SS |
| **French** | É, È, Ê, Ç, Ô, Â | E, E, E, C, O, A |
| **Spanish** | Ñ, Á, É, Í, Ó, Ú | N, A, E, I, O, U |
| **Portuguese** | Ã, Õ, Ç, Á, É | A, O, C, A, E |
| **Nordic** | Å, Ä, Ö, Ø, Æ | A, A, O, O, AE |
| **Hungarian** | Á, É, Í, Ó, Ö, Ő, Ú, Ü, Ű | A, E, I, O, O, O, U, U, U |
| **Turkish** | Ç, Ğ, İ, Ö, Ş, Ü | C, G, I, O, S, U |
| **Romanian** | Ă, Â, Î, Ș, Ț | A, A, I, S, T |
**REAL-WORLD EXAMPLE** (Czech institution):
```yaml
# INCORRECT - Contains diacritics:
ghcid_current: CZ-VY-TEL-L-VHSPAOČRZS # ❌ Contains "Č"
# CORRECT - ASCII only:
ghcid_current: CZ-VY-TEL-L-VHSPAOCRZS # ✅ "Č" → "C"
```
**IMPLEMENTATION**:
```python
import unicodedata
def normalize_diacritics(text: str) -> str:
"""Normalize diacritics to ASCII equivalents."""
# NFD decomposition separates base characters from combining marks
normalized = unicodedata.normalize('NFD', text)
# Remove combining marks (category 'Mn' = Mark, Nonspacing)
ascii_text = ''.join(c for c in normalized if unicodedata.category(c) != 'Mn')
return ascii_text
# Example
normalize_diacritics("VHSPAOČRZS") # Returns "VHSPAOCRZS"
```
**EXAMPLES**:
| Emic Name (with diacritics) | Abbreviation | Wrong |
|-----------------------------|--------------|-------|
| Vlastivědné muzeum v Šumperku | VMS | VMŠ ❌ |
| Österreichische Nationalbibliothek | ON | ÖN ❌ |
| Bibliothèque nationale de France | BNF | BNF (OK - è not in first letter) |
| Múzeum Łódzkie | ML | MŁ ❌ |
| Þjóðminjasafn Íslands | TI | ÞI ❌ |
**See**: `.opencode/ABBREVIATION_SPECIAL_CHAR_RULE.md` for complete documentation (covers both special characters and diacritics)
### 🚨 CRITICAL: Non-Latin Scripts MUST Be Transliterated Before Abbreviation 🚨
**When generating GHCID abbreviations from institution names in non-Latin scripts (Cyrillic, Chinese, Japanese, Korean, Arabic, Hebrew, Greek, Devanagari, Thai, etc.), the emic name MUST first be transliterated to Latin characters using ISO or recognized standards.**
This rule affects **170 institutions** across **21 languages** with non-Latin writing systems.
**CORE PRINCIPLE**: The emic name is PRESERVED in original script in `custodian_name.emic_name`. Transliteration is only used for abbreviation generation.
**TRANSLITERATION STANDARDS BY SCRIPT**:
| Script | Languages | Standard | Example |
|--------|-----------|----------|---------|
| **Cyrillic** | ru, uk, bg, sr, kk | ISO 9:1995 | Институт → Institut |
| **Chinese** | zh | Hanyu Pinyin (ISO 7098) | 东巴文化博物院 → Dongba Wenhua Bowuyuan |
| **Japanese** | ja | Modified Hepburn | 国立博物館 → Kokuritsu Hakubutsukan |
| **Korean** | ko | Revised Romanization | 독립기념관 → Dongnip Ginyeomgwan |
| **Arabic** | ar, fa, ur | ISO 233-2/3 | المكتبة الوطنية → al-Maktaba al-Wataniya |
| **Hebrew** | he | ISO 259-3 | ארכיון → Arkhiyon |
| **Greek** | el | ISO 843 | Μουσείο → Mouseio |
| **Devanagari** | hi, ne | ISO 15919 | राजस्थान → Rajasthana |
| **Bengali** | bn | ISO 15919 | বাংলাদেশ → Bangladesh |
| **Thai** | th | ISO 11940-2 | สำนักหอ → Samnak Ho |
| **Armenian** | hy | ISO 9985 | Մdelays → Matenadaran |
| **Georgian** | ka | ISO 9984 | ხელნაწერთა → Khelnawerti |
**WORKFLOW**:
```
1. Emic Name (original script)
↓
2. Transliterate to Latin (ISO standard)
↓
3. Normalize diacritics (remove accents)
↓
4. Skip articles/prepositions
↓
5. Extract first letters → Abbreviation
```
**EXAMPLES**:
| Language | Emic Name | Transliterated | Abbreviation |
|----------|-----------|----------------|--------------|
| **Russian** | Институт восточных рукописей РАН | Institut Vostochnykh Rukopisey RAN | IVRR |
| **Chinese** | 东巴文化博物院 | Dongba Wenhua Bowuyuan | DWB |
| **Korean** | 독립기념관 | Dongnip Ginyeomgwan | DG |
| **Hindi** | राजस्थान प्राच्यविद्या प्रतिष्ठान | Rajasthana Pracyavidya Pratishthana | RPP |
| **Arabic** | المكتبة الوطنية للمملكة المغربية | al-Maktaba al-Wataniya lil-Mamlaka | MWMM |
| **Hebrew** | ארכיון הסיפור העממי בישראל | Arkhiyon ha-Sipur ha-Amami | ASAY |
| **Greek** | Αρχαιολογικό Μουσείο Θεσσαλονίκης | Archaiologiko Mouseio Thessalonikis | AMT |
**SCRIPT-SPECIFIC SKIP WORDS**:
| Language | Skip Words (Articles/Prepositions) |
|----------|-------------------------------------|
| **Arabic** | al- (the), bi-, li-, fi- (prepositions) |
| **Hebrew** | ha- (the), ve- (and), be-, le-, me- |
| **Persian** | -e, -ye (ezafe connector), va (and) |
| **CJK** | None (particles integral to meaning) |
**IMPLEMENTATION**:
```python
from transliteration import transliterate_for_abbreviation
# Input: emic name in non-Latin script + language code
emic_name = "Институт восточных рукописей РАН"
lang = "ru"
# Step 1: Transliterate to Latin using ISO standard
latin = transliterate_for_abbreviation(emic_name, lang)
# Result: "Institut Vostochnykh Rukopisey RAN"
# Step 2: Apply standard abbreviation extraction
abbreviation = extract_abbreviation_from_name(latin, skip_words={'vostochnykh'})
# Result: "IVRRAN"
```
**GRANDFATHERING POLICY**: Existing abbreviations from 817 UNESCO MoW custodians are grandfathered. This transliteration standard applies only to **NEW custodians** created after December 2025.
**See**: `.opencode/TRANSLITERATION_STANDARDS.md` for complete ISO standards, mapping tables, and Python implementation
---
GHCID uses a **four-identifier strategy** for maximum flexibility and transparency:
### Four Identifier Formats
1. **UUID v5 (SHA-1)** - **PRIMARY** persistent identifier
- Deterministic (same GHCID string → same UUID)
- RFC 4122 standard, universal library support
- Transparent algorithm (anyone can verify)
- Field: `ghcid_uuid`
2. **UUID v8 (SHA-256)** - Secondary persistent identifier (future-proofing)
- Deterministic with stronger cryptographic hash
- SOTA security compliance
- Field: `ghcid_uuid_sha256`
3. **UUID v7** - Database record ID ONLY (NOT for persistent identification)
- Time-ordered for database performance
- NOT deterministic (different each time)
- Use for database primary keys, NOT for citations or cross-system references
- Field: `record_id`
4. **Numeric (64-bit)** - Compact identifier for CSV exports
- Deterministic (SHA-256 → 64-bit integer)
- Database optimization, spreadsheet-friendly
- Field: `ghcid_numeric`
### Critical Understanding: UUID v5 is Primary
**Why UUID v5 (SHA-1) over UUID v8 (SHA-256)?**
The primary identifier is **UUID v5** because:
- ✅ **Transparency** - Anyone can verify using standard `uuid.uuid5()` function
- ✅ **Reproducibility** - No custom algorithm to share, RFC 4122 defines it
- ✅ **Interoperability** - Every programming language has built-in UUID v5 support
- ✅ **Community Trust** - Public, standardized algorithm builds confidence
**SHA-1 Safety for Identifiers:**
SHA-1 is deprecated for **cryptographic security** (digital signatures, TLS, passwords) but **appropriate for identifier generation**:
- Heritage institution identifiers are **non-adversarial** (no attacker trying to forge museum IDs)
- 128-bit collision resistance is sufficient (P(collision) ≈ 1.5×10^-29 for 1M institutions)
- RFC 4122 (UUID v5) remains active standard (not deprecated by IETF)
- See **[Why GHCID Uses UUID v5 and SHA-1](docs/WHY_UUID_V5_SHA1.md)** for detailed rationale
**Future-Proofing:**
- We generate **both UUID v5 and UUID v8** for every institution
- Can migrate to SHA-256 primary if RFC 4122 is updated
- Both are deterministic - no data loss in migration
### When Extracting Data
Agents should generate ALL four identifiers for every institution:
```yaml
# Example extraction output
- id: https://w3id.org/heritage/custodian/br/bnb-001
name: Biblioteca Nacional do Brasil
ghcid: BR-RJ-RIO-L-BNB
ghcid_uuid: "550e8400-e29b-41d4-a716-446655440000" # UUID v5 - PRIMARY
ghcid_uuid_sha256: "a1b2c3d4-e5f6-8a1b-9c2d-3e4f5a6b7c8d" # UUID v8 - Secondary
ghcid_numeric: 213324328442227739 # 64-bit numeric
# Note: UUID v7 (record_id) generated at database insertion, not during extraction
```
### GHCID Collision Handling for AI Agents
**CRITICAL**: When extracting heritage institution data, AI agents MUST understand and apply temporal collision resolution rules to maintain PID stability.
#### The Collision Problem
Multiple institutions may generate the same base GHCID (before name suffix addition):
- Two museums in Amsterdam abbreviated "SM": `NL-NH-AMS-M-SM`
- Two historical societies in Utrecht: `NL-UT-UTR-S-HK`
- Two libraries in São Paulo abbreviated "BM": `BR-SP-SAO-L-BM`
#### Decision Tree for Collision Resolution
When extracting data, agents should follow this decision process:
```
1. Generate base GHCID (without name suffix)
↓
2. Check if base GHCID exists in published dataset
↓
NO → Use base GHCID as-is, record extraction_date
↓
YES → Temporal priority check
↓
3. Compare extraction_date with existing publication_date
↓
SAME DATE (batch import) → First Batch Collision
├─ ALL institutions get name suffixes
├─ Convert native language name to snake_case
└─ Append to GHCID: NL-NH-AMS-M-SM-stedelijk_museum_amsterdam
↓
LATER DATE (historical addition) → Historical Addition
├─ PRESERVE existing GHCID (no modification)
├─ ONLY new institution gets name suffix
└─ New GHCID: NL-NH-AMS-M-SM-science_museum_amsterdam
```
#### Implementation Rules for Agents
**Rule 1: Always Track Provenance Timestamp**
```yaml
provenance:
data_source: CONVERSATION_NLP
data_tier: TIER_4_INFERRED
extraction_date: "2025-11-15T14:30:00Z" # ← REQUIRED for collision detection
extraction_method: "AI agent NER extraction"
confidence_score: 0.92
```
**Rule 2: Detect Collisions by Base GHCID**
Before adding name suffixes, group institutions by base GHCID:
```python
# Collision detection pseudocode for agents
base_ghcid = generate_base_ghcid(institution) # Without name suffix
existing_records = published_dataset.filter(base_ghcid=base_ghcid)
if len(existing_records) > 0:
# Collision detected - apply temporal priority
apply_collision_resolution(institution, existing_records)
```
**Rule 3: First Batch - ALL Get Name Suffixes**
If ALL colliding institutions have the **same** `extraction_date`:
```yaml
# Example: 2025-11-01 batch import discovers two institutions
- name: Stedelijk Museum Amsterdam
ghcid: NL-NH-AMS-M-SM-stedelijk_museum_amsterdam # Gets name suffix
provenance:
extraction_date: "2025-11-01T10:00:00Z"
- name: Science Museum Amsterdam
ghcid: NL-NH-AMS-M-SM-science_museum_amsterdam # Gets name suffix
provenance:
extraction_date: "2025-11-01T10:00:00Z" # Same date = first batch
```
**Rule 4: Historical Addition - ONLY New Gets Name Suffix**
If new institution's `extraction_date` is **later** than existing record:
```yaml
# EXISTING (2025-11-01, already published):
- name: Hermitage Amsterdam
ghcid: NL-NH-AMS-M-HM # ← NO CHANGE (PID stability!)
provenance:
extraction_date: "2025-11-01T10:00:00Z"
# NEW (2025-11-15, historical addition):
- name: Historical Museum Amsterdam
ghcid: NL-NH-AMS-M-HM-historical_museum_amsterdam # ← ONLY new gets name suffix
provenance:
extraction_date: "2025-11-15T14:30:00Z"
```
#### Name Suffix Generation
**Converting institution names to snake_case suffixes:**
```python
import re
import unicodedata
def generate_name_suffix(native_name: str) -> str:
"""Convert native language institution name to snake_case suffix.
Examples:
"Stedelijk Museum Amsterdam" → "stedelijk_museum_amsterdam"
"Musée d'Orsay" → "musee_dorsay"
"Österreichische Nationalbibliothek" → "osterreichische_nationalbibliothek"
"""
# Normalize unicode (NFD decomposition) and remove diacritics
normalized = unicodedata.normalize('NFD', native_name)
ascii_name = ''.join(c for c in normalized if unicodedata.category(c) != 'Mn')
# Convert to lowercase
lowercase = ascii_name.lower()
# Remove apostrophes, commas, and other punctuation
no_punct = re.sub(r"[''`\",.:;!?()[\]{}]", '', lowercase)
# Replace spaces and hyphens with underscores
underscored = re.sub(r'[\s\-]+', '_', no_punct)
# Remove any remaining non-alphanumeric characters (except underscores)
clean = re.sub(r'[^a-z0-9_]', '', underscored)
# Collapse multiple underscores
final = re.sub(r'_+', '_', clean).strip('_')
return final
```
**Name suffix rules**:
- Use the institution's **full official name** in its **native language**
- Transliterate non-Latin scripts to ASCII (e.g., Pinyin for Chinese)
- Remove all diacritics (é → e, ö → o, ñ → n)
- Remove punctuation (apostrophes, commas, periods)
- Replace spaces with underscores
- All lowercase
#### GHCID History Tracking
When name suffix is added to resolve collision, update `ghcid_history`:
```yaml
ghcid_history:
- ghcid: NL-NH-AMS-M-HM-historical_museum_amsterdam # Current (with name suffix)
ghcid_numeric: 789012345678
valid_from: "2025-11-15T14:30:00Z" # When name suffix added
valid_to: null
reason: "Name suffix added to resolve collision with existing NL-NH-AMS-M-HM (Hermitage Amsterdam)"
- ghcid: NL-NH-AMS-M-HM # Original (without name suffix)
ghcid_numeric: 123456789012
valid_from: "2025-11-15T14:00:00Z" # When first extracted
valid_to: "2025-11-15T14:30:00Z" # When collision detected
reason: "Base GHCID from geographic location and institution name"
```
#### PID Stability Principle - "Cool URIs Don't Change"
**NEVER modify a published GHCID.** Once exported to RDF, JSON-LD, or CSV, a GHCID becomes a persistent identifier that may be:
- **Cited in academic papers** - Journal articles referencing heritage collections
- **Used in external APIs** - Third-party systems querying our data
- **Embedded in linked data** - RDF triples in knowledge graphs
- **Referenced in finding aids** - Archival descriptions linking to institutions
Changing a published GHCID breaks these external references. Per W3C "Cool URIs Don't Change":
- ✅ **Correct**: Add name suffix to NEW institution (historical addition)
- ❌ **WRONG**: Retroactively add name suffix to EXISTING published GHCID
#### Error Handling for Agents
**Scenario 1: Missing Provenance Timestamp**
```python
if 'extraction_date' not in institution['provenance']:
# Use current timestamp as fallback
institution['provenance']['extraction_date'] = datetime.now(timezone.utc).isoformat()
# Log warning for manual review
log.warning(f"Missing extraction_date for {institution['name']}, using current time")
```
**Scenario 2: Multiple Historical Additions**
```python
# Three institutions generate NL-UT-UTR-S-HK
# Extraction dates: 2025-11-01, 2025-11-15, 2025-12-01
# Result:
# 2025-11-01: NL-UT-UTR-S-HK (first, no name suffix)
# 2025-11-15: NL-UT-UTR-S-HK-historische_kring_utrecht (second, gets name suffix)
# 2025-12-01: NL-UT-UTR-S-HK-heemkundige_kring_utrecht (third, gets name suffix)
```
**Scenario 3: Collision Resolution with Name Suffix**
```python
if collision_detected:
# Generate name suffix from native language name
name_suffix = generate_name_suffix(institution['name'])
# Append to base GHCID
ghcid = f"{base_ghcid}-{name_suffix}" # e.g., NL-NH-AMS-M-HM-historical_museum_amsterdam
# Record collision resolution
institution['provenance']['notes'] = (
f"Name suffix added to resolve collision with existing {base_ghcid}."
)
```
#### Validation Checklist for Agents
Before publishing extracted data, verify:
- [ ] All institutions have `extraction_date` in provenance metadata
- [ ] Collisions detected by grouping on base GHCID (without name suffix)
- [ ] First batch collisions: ALL instances have name suffixes
- [ ] Historical additions: ONLY new instances have name suffixes
- [ ] No published GHCIDs modified (PID stability test)
- [ ] GHCID history entries created with valid temporal ordering
- [ ] Name suffixes derived from native language institution names
- [ ] Collision reasons documented in `ghcid_history`
#### Example Extraction Prompts for Agents
**Prompt Template for NLP Extraction**:
```
Extract heritage institutions from this conversation about [REGION] GLAM institutions.
For EACH institution:
1. Generate base GHCID using geographic location and institution type
2. Check for collisions with previously published GHCIDs
3. Apply temporal priority rule:
- If collision with same extraction_date → First Batch (all get name suffixes)
- If collision with earlier publication_date → Historical Addition (only new gets name suffix)
4. Generate snake_case name suffix from native language institution name
5. Create GHCID history entry documenting collision resolution
6. Include extraction_date in provenance metadata
Output: LinkML-compliant YAML with complete collision handling
```
**Prompt Template for CSV Parsing**:
```
Parse this heritage institution CSV file dated [DATE].
All rows have the same extraction_date ([DATE]).
If multiple institutions generate the same base GHCID:
- This is a FIRST BATCH collision
- ALL colliding institutions MUST receive name suffixes
- Generate name suffix from institution's native language name
- Document collision in ghcid_history
Output: YAML with collision resolution applied
```
#### Testing Strategies for Collision Handling
**Unit Test: First Batch Collision**
```python
def test_first_batch_collision():
"""Two institutions extracted same day with same base GHCID."""
institutions = [
{
'name': 'Stedelijk Museum Amsterdam',
'base_ghcid': 'NL-NH-AMS-M-SM',
'identifiers': [{'identifier_scheme': 'Wikidata', 'identifier_value': 'Q621531'}],
'provenance': {'extraction_date': '2025-11-01T10:00:00Z'}
},
{
'name': 'Science Museum Amsterdam',
'base_ghcid': 'NL-NH-AMS-M-SM',
'identifiers': [{'identifier_scheme': 'Wikidata', 'identifier_value': 'Q98765432'}],
'provenance': {'extraction_date': '2025-11-01T10:00:00Z'}
}
]
resolved = resolve_collisions(institutions)
# Both should have name suffixes
assert resolved[0]['ghcid'] == 'NL-NH-AMS-M-SM-stedelijk_museum_amsterdam'
assert resolved[1]['ghcid'] == 'NL-NH-AMS-M-SM-science_museum_amsterdam'
```
**Unit Test: Historical Addition**
```python
def test_historical_addition():
"""New institution added later with same base GHCID."""
published = {
'name': 'Hermitage Amsterdam',
'ghcid': 'NL-NH-AMS-M-HM', # Already published
'provenance': {'extraction_date': '2025-11-01T10:00:00Z'}
}
new_institution = {
'name': 'Historical Museum Amsterdam',
'base_ghcid': 'NL-NH-AMS-M-HM', # Collision!
'identifiers': [{'identifier_scheme': 'Wikidata', 'identifier_value': 'Q17339437'}],
'provenance': {'extraction_date': '2025-11-15T14:30:00Z'}
}
resolved = resolve_collision(new_institution, published_dataset=[published])
# Published GHCID unchanged
assert published['ghcid'] == 'NL-NH-AMS-M-HM'
# New institution gets name suffix
assert resolved['ghcid'] == 'NL-NH-AMS-M-HM-historical_museum_amsterdam'
# GHCID history created
assert len(resolved['ghcid_history']) == 2
assert resolved['ghcid_history'][0]['ghcid'] == 'NL-NH-AMS-M-HM-historical_museum_amsterdam'
```
#### References for Collision Handling
- **Specification**: `docs/PERSISTENT_IDENTIFIERS.md` - "Historical Collision Resolution" section
- **Algorithm**: `docs/plan/global_glam/07-ghcid-collision-resolution.md` - Temporal dimension and decision logic
- **Examples**: `docs/GHCID_PID_SCHEME.md` - Timeline examples with real institutions
- **Implementation**: `scripts/regenerate_historical_ghcids.py` - Code comments documenting collision handling
- **Schema**: `schemas/provenance.yaml` - `GHCIDHistoryEntry` and `ChangeEvent` classes
**See also:**
- `docs/PERSISTENT_IDENTIFIERS.md` - Complete identifier format documentation
- `docs/UUID_STRATEGY.md` - UUID v5 vs v7 vs v8 comparison
- `docs/WHY_UUID_V5_SHA1.md` - SHA-1 safety rationale
---
## References
- **Schema (v0.2.0)**:
- Main: `schemas/heritage_custodian.yaml`
- Core classes: `schemas/core.yaml`
- Enumerations: `schemas/enums.yaml`
- Provenance: `schemas/provenance.yaml`
- Collections: `schemas/collections.yaml`
- Dutch extensions: `schemas/dutch.yaml`
- Architecture: `/docs/SCHEMA_MODULES.md`
- **Persistent Identifiers**:
- Overview: `docs/PERSISTENT_IDENTIFIERS.md`
- UUID Strategy: `docs/UUID_STRATEGY.md`
- SHA-1 Rationale: `docs/WHY_UUID_V5_SHA1.md`
- GHCID PID Scheme: `docs/GHCID_PID_SCHEME.md`
- Collision Resolution: `docs/plan/global_glam/07-ghcid-collision-resolution.md`
- **Architecture**: `docs/plan/global_glam/02-architecture.md`
- **Data Standardization**: `docs/plan/global_glam/04-data-standardization.md`
- **Design Patterns**: `docs/plan/global_glam/05-design-patterns.md`
- **Dependencies**: `docs/plan/global_glam/03-dependencies.md`
---
**Version**: 0.2.1
**Schema Version**: v0.2.1 (modular)
**Last Updated**: 2025-12-08
**Maintained By**: GLAM Data Extraction Project
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