id: https://nde.nl/ontology/hc/class/DetectedEntity
name: DetectedEntity
title: Detected Entity Class
prefixes:
  linkml: https://w3id.org/linkml/
  hc: https://nde.nl/ontology/hc/
  prov: http://www.w3.org/ns/prov#
  schema: http://schema.org/
  crm: http://www.cidoc-crm.org/cidoc-crm/
  skos: http://www.w3.org/2004/02/skos/core#
default_prefix: hc
imports:
  - linkml:types
  - ../slots/geographic_extent
  - ../slots/identified_by
  - ../slots/has_label
  - ../slots/has_time_interval
  - ../slots/has_type
  - ../slots/generated_by
classes:
  DetectedEntity:
    class_uri: prov:Entity
    description: "Base class for entities detected through automated analysis processes.\n\
      \n**DEFINITION**:\n\nDetectedEntity represents items identified by automated\
      \ analysis pipelines\n(computer vision, NLP, audio analysis, etc.). Each detected\
      \ entity captures:\n- What was detected (via has_type → specific Type\
      \ class)\n- Detection confidence (probability score)\n- Temporal/spatial location\
      \ (timestamps, bounding boxes)\n\n**CRITICAL: DETECTION vs TYPE**\n\n| Aspect\
      \ | DetectedEntity (This Class) | *Type Classes (Categories) |\n|--------|----------------------------|---------------------------|\n\
      | **Nature** | Detection result | Classification |\n| **Examples** | \"Transition\
      \ at 00:03:15\" | FADE_IN, CUT, DISSOLVE |\n| **Properties** | Confidence, timestamps\
      \ | Semantic metadata |\n| **Cardinality** | Many instances per video | ~20-30\
      \ types |\n\n**USE CASE - Video Annotation**:\n\n```\nVideoAnnotation\n  │\n\
      \  └── has_detected → DetectedEntity[]\n        ├── has_type →\
      \ TransitionType (FADE_IN, CUT, etc.)\n        ├── confidence_score → 0.95\n\
      \        ├── start_time → \"00:03:15\"\n        └── end_time → \"00:03:17\"\n\
      ```\n\n**USE CASE - Text Detection**:\n\n```\nVideoAnnotation\n  │\n  └── has_detected\
      \ → DetectedEntity[]\n        ├── has_type → TextType (TITLE, CAPTION,\
      \ etc.)\n        ├── confidence_score → 0.87\n        ├── has_geographic_extent\
      \ → [x1, y1, x2, y2]\n        └── has_label → \"Museum Tour\"\n```\n\n\
      **ONTOLOGY ALIGNMENT**:\n\n- **PROV-O Entity**: Detected entities are generated\
      \ by activities\n- **W3C Web Annotation**: Compatible with annotation body/target\
      \ patterns\n- **CIDOC-CRM E73_Information_Object**: Information derived from\
      \ sources\n\n**RELATIONSHIP TO SLOTS**:\n\n- `has_detected`: Links from\
      \ VideoAnnotation → DetectedEntity[]\n- `has_type`: Links DetectedEntity\
      \ → TransitionType, TextType, etc.\n- Uses shared slots for identifier, label,\
      \ confidence\n"
    abstract: false
    exact_mappings:
    - prov:Entity
    close_mappings:
    - crm:E73_Information_Object
    - schema:DataDownload
    related_mappings:
    - schema:Thing
    - crm:E21_Person
    slots:
    - identified_by
    - has_label
    - has_type
    - generated_by
    - has_time_interval
    - geographic_extent
    - begin_of_the_begin
    slot_usage:
      identified_by:
#         range: string
        required: false
        examples:
        - value: detection-001
        - value: transition-fade-00:03:15
      has_type:
#         range: string
        required: true
        examples:
        - value: TransitionType:FADE_IN
        - value: TextType:TITLE
      has_label:
#         range: string
        required: false
        examples:
        - value: Museum Tour
        - value: Opening Credits
      generated_by:
        range: GenerationEvent
        required: false
        inlined: true
        description: 'Generation event containing confidence score for entity detection.
          MIGRATED 2026-01-19: Replaces confidence_score slot with structured pattern.'
        examples:
        - value:
            has_score:
              has_score: 0.95
              has_method: computer_vision
        - value:
            has_score:
              has_score: 0.62
              has_method: ocr_extraction
      has_time_interval:
        range: TimeInterval
        required: false
        inlined: true
        examples:
        - value: null
      geographic_extent:
        range: float
        multivalued: true
        required: false
        examples:
        - value:
          - 100.0
          - 50.0
          - 400.0
          - 150.0
    annotations:
      specificity_score: '0.65'
      specificity_rationale: Fairly specific - detection entities are domain-relevant
        for media analysis.
      has_score: null
      replaces_pattern: 'Replaces inline transition_types_detected and text_types_detected
        slots

        with structured DetectedEntity → has_type → Type pattern.

        '
      custodian_types: '[''*'']'
    comments:
    - Base class for automated detection results
    - Use has_type to link to specific Type classes (TransitionType, TextType)
    - 'Captures detection metadata: confidence (via generated_by), timestamps,
      bounding boxes'
    - Generated by detection activities (prov:wasGeneratedBy pattern)
    - 'Rule 39 compliant: uses has_* slot naming (migrated from has_or_had_*)'
    - 'MIGRATED 2026-01-19: confidence_score → generated_by + ConfidenceScore'
    see_also:
    - https://www.w3.org/TR/prov-o/#Entity
    - https://www.w3.org/TR/annotation-model/
    - http://www.cidoc-crm.org/cidoc-crm/E73_Information_Object
    examples:
    - value:
        identified_by: transition-001
        has_type: TransitionType:FADE_IN
        generated_by:
          has_score:
            has_score: 0.95
            has_method: video_analysis
        start_time: 00:03:15
        has_time_interval: null
    - value:
        identified_by: text-001
        has_type: TextType:TITLE
        has_label: Museum Tour Guide
        generated_by:
          has_score:
            has_score: 0.87
            has_method: ocr_extraction
        has_geographic_extent:
        - 100.0
        - 50.0
        - 400.0
        - 150.0