100 lines
4.4 KiB
YAML
100 lines
4.4 KiB
YAML
id: https://nde.nl/ontology/hc/class/ExtractionMetadata
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name: extraction_metadata_class
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title: Extraction Metadata Class
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version: 1.0.0
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prefixes:
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linkml: https://w3id.org/linkml/
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hc: https://nde.nl/ontology/hc/
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schema: http://schema.org/
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prov: http://www.w3.org/ns/prov#
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dct: http://purl.org/dc/terms/
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xsd: http://www.w3.org/2001/XMLSchema#
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imports:
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- linkml:types
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- ../enums/ProfileExtractionMethodEnum
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- ../metadata
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- ../slots/has_or_had_expense
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- ../slots/has_or_had_identifier
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- ../slots/has_or_had_method
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- ../slots/has_or_had_score
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- ../slots/has_or_had_source
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- ../slots/has_or_had_url
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- ../slots/is_or_was_retrieved_by
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- ../slots/llm_response
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- ../slots/retrieval_timestamp
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# default_range: string
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classes:
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ExtractionMetadata:
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class_uri: prov:Activity
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description: "Provenance metadata for data extraction activities.\n\nRecords how, when, and by what agent data was extracted from \nexternal sources (LinkedIn, web scraping, APIs).\n\n**PROV-O Alignment**:\n- ExtractionMetadata IS a prov:Activity (the extraction process)\n- The extracted data IS the prov:Entity (output of the activity)\n- is_or_was_retrieved_by IS the prov:Agent (software/AI that performed extraction)\n- has_or_had_source/has_or_had_url IS prov:used (input to the activity)\n\n**Use Cases**:\n- LinkedIn profile extractions via Exa API\n- Web scraping provenance\n- Staff list parsing provenance\n- Connection network extraction\n\n**Example JSON Structure**:\n```json\n{\n \"extraction_metadata\": {\n \"has_or_had_source\": \"/path/to/source.json\",\n \"has_or_had_identifier\": \"org_staff_0001_name\",\n \"retrieval_timestamp\": \"2025-12-12T22:00:00Z\",\n \"has_or_had_method\": \"exa_crawling_exa\",\n \"is_or_was_retrieved_by\": \"claude-opus-4.5\",\n \"has_or_had_url\": \"https://www.linkedin.com/in/...\"\
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,\n \"has_or_had_expense\": 0.001\n }\n}\n```\n"
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exact_mappings:
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- prov:Activity
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close_mappings:
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- schema:Action
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- dct:ProvenanceStatement
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slots:
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- has_or_had_expense
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- is_or_was_retrieved_by
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- retrieval_timestamp
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- has_or_had_method
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- has_or_had_url
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- llm_response
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- has_or_had_identifier
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- has_or_had_source
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- has_or_had_score
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slot_usage:
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has_or_had_source:
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# range: string
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examples:
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- value: /data/custodian/person/affiliated/parsed/rijksmuseum_staff_20251210T155416Z.json
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has_or_had_identifier:
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# range: string
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pattern: ^[a-z0-9-]+_staff_[a-z0-9-_]+$
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examples:
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- value: rijksmuseum_staff_0042_jan_van_der_berg
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- value: exa_12345678-abcd-efgh-ijkl-mnopqrstuv
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retrieval_timestamp:
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range: datetime
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required: true
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examples:
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- value: '2025-12-12T22:00:00Z'
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has_or_had_method:
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range: ProfileExtractionMethodEnum
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required: true
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examples:
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- value: exa_crawling_exa
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is_or_was_retrieved_by:
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# range: string
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examples:
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- value: claude-opus-4.5
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- value: ''
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has_or_had_url:
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range: uri
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pattern: ^https://www\.linkedin\.com/in/[a-z0-9-]+/?$
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examples:
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- value: https://www.linkedin.com/in/jan-van-der-berg-12345
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has_or_had_expense:
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range: float
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minimum_value: 0.0
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examples:
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- value: 0.001
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- value: 0.0
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llm_response:
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range: LLMResponse
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required: false
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inlined: true
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examples:
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- value: "{\n \"content\": \"Extracted institution data...\",\n \"reasoning_content\": \"Analyzing the input for LinkML schema conformity...\",\n \"thinking_mode\": \"preserved\",\n \"clear_thinking\": false,\n \"model\": \"glm-4.7\",\n \"provider\": \"zai\",\n \"created\": \"2025-12-23T10:30:00Z\",\n \"prompt_tokens\": 150,\n \"completion_tokens\": 450,\n \"total_tokens\": 600,\n \"finish_reason\": \"stop\",\n \"cost_usd\": 0.0\n}\n"
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comments:
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- Every person entity file MUST have extraction_metadata
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- See AGENTS.md Rule 20 for required fields
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- is_or_was_retrieved_by should be 'claude-opus-4.5' for manual extraction
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- has_or_had_expense enables budget tracking for API-heavy extractions
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see_also:
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- https://www.w3.org/TR/prov-o/
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- https://docs.exa.ai/
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annotations:
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specificity_score: 0.1
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specificity_rationale: Generic utility class/slot created during migration
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custodian_types: "['*']"
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