- Rename 512 person files from XX-XX-XXX placeholders to proper GeoNames locations
- Update 2,463 profiles with enriched data
- Add 512 new person profiles (AU, international heritage professionals)
- PPID format: ID_{birth-loc}_{decade}_{work-loc}_{custodian}_{NAME}
- Add 'Compare' toggle button next to slots with slot_usage overrides
- Show generic slot definition vs class-specific override in 3-column grid
- Highlight changed properties with green 'changed' badge
- Display '(inherited)' when override matches generic definition
- Display '(not defined)' when generic has no value for property
- Compare: range, description, required, multivalued, slot_uri, pattern, identifier
- Full i18n support (Dutch/English translations)
- Responsive design: stacks vertically on mobile (<640px)
- Detect COUNT queries by checking for 'count' key in SPARQL results
- Skip institution transformation for COUNT queries to preserve count value
- Fixes bug where 'Hoeveel archieven in Utrecht?' returned 1 instead of 10
- COUNT queries now correctly extract integer count from SPARQL response
- Fix bug where COUNT queries showed Qdrant result count (10) instead of
actual SPARQL count (e.g., 204 musea in Noord-Holland)
- Use sparql_results for count extraction in factual query fast-path
- Also fix fallback COUNT/LIST handling to use sparql_results
- Add display_name and name_romanized fields to all 7948 person profiles
- Resolve UNKNOWN-UNKNOWN collision group (Hebrew/Arabic names now properly romanize)
- Hebrew names like אבישי דנינו now generate PPID AVISHI-DANINO instead of UNKNOWN-UNKNOWN
- Collision count reduced from 82 to 81 groups
Regenerated using generate_ppids.py with unidecode support (commit abe30cb)
Merge data from PENDING files (with XX-XXX placeholders) into their
corresponding enriched custodian records with proper GHCIDs.
Countries affected:
- DE: 4 institutions (Deutsche Stiftung, Jewish Museum Berlin, etc.)
- ES: 1 institution (Biblioteca Nacional de España)
- FR: 1 institution (NMO)
- ID: 18 Indonesian museums and archives
- NL: 111 Dutch institutions across all provinces
- US: 1 institution (ARCA)
The PENDING files are deleted after merge; originals archived in
data/custodian/archive/pending_merged_20250109/
- Add green 'slot_usage' badge for slots with class-specific overrides
- Add ✦ markers next to properties that are overridden vs inherited
- Add green left border styling for slots with slot_usage
- Add i18n translations (nl/en) for override indicators
- Merge generic slot definitions with class-specific slot_usage properties
This helps users understand which slot properties come from the generic
slot definition vs which are overridden at the class level via slot_usage.
Identified 125 institutions from LinkedIn staff extraction that are NOT Dutch:
- FR: 45 (French museums, archives, libraries)
- ID: 14 (Indonesian institutions)
- GB: 14 (British institutions)
- DE: 13 (German museums, foundations)
- BE: 11 (Belgian museums)
- IT: 6 (Italian institutions)
- AU: 6 (Australian archives, museums)
- Plus smaller counts from IN, US, ES, CH, DK, AT, SA, NO, IL
These files have staff data from LinkedIn company pages but need
GHCID resolution (currently XX-XXX placeholders for region/city).
Dutch PENDING files remain: 1,283
Add final two chapters of the Person PID (PPID) design document:
- 08_implementation_guidelines.md: Database architecture, API design,
data ingestion pipeline, GHCID integration, security, performance,
technology stack, deployment, and monitoring specifications
- 09_governance_and_sustainability.md: Data governance policies,
quality assurance, sustainability planning, community engagement,
legal considerations, and long-term maintenance strategies
Merged LinkedIn-extracted staff sections from PENDING files into their
corresponding proper GHCID custodian files. This consolidates data from
two extraction sources:
- Existing enriched files: Google Maps, Museum Register, YouTube, etc.
- PENDING files: LinkedIn staff data extraction
Files modified:
- 28 custodian files enriched with staff data
- 35 PENDING files deleted (merged into proper locations)
- Originals archived to archive/pending_duplicates_20250109/
Key institutions enriched:
- Rijksmuseum (NL-NH-AMS-M-RM)
- Stedelijk Museum Amsterdam (NL-NH-AMS-M-SMA)
- Amsterdam Museum (NL-NH-AMS-M-AM)
- Regionaal Archief Alkmaar (NL-NH-ALK-A-RAA)
- Maritiem Museum Rotterdam (NL-ZH-ROT-M-MMR)
- And 23 more museums/archives across NL
New scripts:
- scripts/merge_staff_data.py: Automated staff data merger
- scripts/categorize_pending_files.py: PENDING file analysis utility
Add databases: ["oxigraph"] to 5 more templates that don't benefit from vector search:
- count_institutions_by_type_location
- compare_locations
- find_by_founding
- find_custodians_by_budget_threshold
- find_institutions_by_founding_date
Total templates with Oxigraph-only routing: 10
Script to resolve NL-XX-XXX-PENDING files that have city names in filename:
- Looks up city in GeoNames database
- Updates YAML with location data (city, region, country)
- Generates proper GHCID with UUID v5/v8
- Renames files to match new GHCID
- Archives original PENDING files for reference
Log database routing decisions and add databases_used to response metadata.
When template specifies databases: ["oxigraph"], Qdrant vector search is skipped.
- Rename NL-FR-HOO-M-MT.yaml → NL-FR-TER-M-MT.yaml
- HOO (Hooghalen) → TER (Terschelling) - correct island location
- Institution is on Terschelling island, not in Drenthe
- Add --type/-t flag to specify institution type (A, G, H, I, L, M, N, O, R, S, T, U, X, ALL)
- Default still Type I (Intangible Heritage) for backward compatibility
- Skip PENDING files that have no location data
- Update help text with all supported types
- Add 'databases' field to TemplateDefinition and TemplateMatchResult
- Support values: 'oxigraph' (SPARQL/KG), 'qdrant' (vector search)
- Add helper methods use_oxigraph() and use_qdrant()
- Default to both databases for backward compatibility
- Allows templates to skip vector search for factual/geographic queries
- Migrate 236+ class files from custodian_types to has_or_had_custodian_type
- Archive deprecated slots: custodian_type, custodian_types, custodian_type_broader/narrower/related
- Update main schema and manifest imports
- Fix Custodian.yaml class to use new slot
- Fix annotation format (list→scalar) in has_or_had_custodian_type.yaml
Rules applied:
- Rule 39: RiC-O naming convention (hasOrHad pattern)
- Rule 43: Slot nouns must be singular (multivalued:true for cardinality)
- Rule 38: Slot centralization with semantic URI
Audit of 188 Type I custodian files revealed:
- 62 false matches (33%) detected and corrected
- Categories: domain mismatch (39), name mismatch (8), wrong location (6),
wrong org type (5), different entity (3), different event (3)
- Documents why Google Maps fails for intangible heritage:
virtual orgs, person-based heritage, volunteer networks, event-based orgs
This validates KIEN as TIER_1_AUTHORITATIVE for Type I custodians.
Manual review of remaining Type I custodian files without official websites
identified additional false matches in these categories:
Wrong organization type:
- Bird catchers vs bird watchers association
- Heritage org vs webshop
- Regional org vs specific local entity
- Federation vs single member association
- Bell ringers org vs church building
Wrong location:
- Amsterdam org matched to Den Haag
- Haarlem org matched to Apeldoorn
- Rotterdam org matched to Amstelveen
- Dutch org matched to Suriname (!)
- Giethoorn event matched to Belt-Schutsloot
- Duindorp bonfire matched to Scheveningen
Different event/entity:
- Horse racing org vs summer festival
- Street name vs organization
- Heritage foundation vs specific local fair
Total Type I false matches fixed: 62 of 188 files (33%)
- Add ontology cache warming at startup in lifespan() function
- Add is_factual_query() detection in template_sparql.py (12 templates)
- Add factual_result and sparql_query fields to DSPyQueryResponse
- Skip LLM generation for factual templates (count, list, compare)
- Execute SPARQL directly and return results as table (~15s → ~2s latency)
- Update ConversationPanel.tsx to render factual results table
- Add CSS styling for factual results with green theme
For queries like 'hoeveel archieven zijn er in Den Haag', the SPARQL
results ARE the answer - no need for expensive LLM prose generation.
Additional Type I custodian files with obvious name mismatches between
KIEN registry entries and Google Maps results. These couldn't be
auto-detected via domain mismatch because they lack official websites.
Fixes:
- Dick Timmerman (person) → carpentry business
- Ria Bos (cigar maker) → money transfer agent
- Stichting Kracom (Krampuslauf) → Happy Caps retail
- Fed. Nederlandse Vertelorganisaties → NET Foundation
- Stichting dodenherdenking Alphen → wrong memorial
- Sao Joao Rotterdam → Heemraadsplein (location not org)
- sport en spel (heritage) → equipment rental
- Eiertikken Ommen → restaurant
Also adds detection and fix scripts for Google Maps false matches.
Per Rule 40 (KIEN authoritative source), Google Maps frequently returns
false matches for intangible heritage organizations. These are virtual
networks without commercial storefronts.
Changes:
- Mark google_maps_enrichment.status as FALSE_MATCH
- Preserve original data in original_false_match for audit trail
- Add correction_timestamp and correction_agent provenance
- Special handling for NL-GE-TIE-I-M (Stichting MOZA): also fixed
YouTube false match (Mozart channel) and removed ~1750 lines of
irrelevant video data
Detection method: Domain mismatch between Google Maps website field
and official KIEN registry website.
Major architectural changes based on Formica et al. (2023) research:
- Add TemplateClassifier for deterministic SPARQL template matching
- Add SlotExtractor with synonym resolution for slot values
- Add TemplateInstantiator using Jinja2 for query rendering
- Refactor dspy_heritage_rag.py to use template system
- Update main.py with streamlined pipeline
- Fix semantic_router.py ordering issues
- Add comprehensive metrics tracking
Template-based approach achieves 65% precision vs 10% LLM-only
per Formica et al. research on SPARQL generation.
Clean up old generated RDF/OWL files that have been superseded by
the clean schema deployed to Oxigraph (commit f8421a2903).
Only the current deployed schema should be regenerated as needed.
