| concepts |
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"end_index": 2088, "surrounding_text": "Dynamic Targeting", "extraction_confidence": 0.85, "extraction_method": "LLM_EXTRACTION", "extracted_at": "2025-12-02T22:06:39.655541Z"}], "source_entity_names": ["Dynamic Targeting"], "hierarchical_evidence": ["Dynamic targeting is a distinct targeting methodology", "DT represents adaptive targeting approaches"], "taxonomic_reasoning": "Dynamic targeting is a specialized targeting process distinct from time-sensitive targeting"}, {"id": "target_processing", "name": "Target Processing", "definition": "Analytical processes for target validation, assessment, and preparation", "parent_concepts": ["targeting_processes"], "confidence_score": 0.89, "entity_id": "E-be8af889450b", "transformation_logs": [{"id": "transform_target_p", "stage": "TRANSFORMATION_STAGE_TAXONOMY_GENERATION", "method": "TRANSFORMATION_METHOD_LLM_PROMPT", "from_state": "classified_entities", "to_state": "taxonomy_concepts", "final_confidence": 0.89, "reasoning": "LLM-generated 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[{"document_id": "batch-ab1be436-e79b-436b-8f65-7bbd5d03d519-c311ea18-6f02-461b-936e-65a4bc6e17c6_chunk_15", "start_index": 3512, "end_index": 3519, "surrounding_text": "focus on complex judgment calls while reducing engagement timelines from hours to minutes.", "extraction_confidence": 0.85, "extraction_method": "LLM_EXTRACTION", "extracted_at": "2025-12-02T22:06:44.666442Z"}], "hierarchical_evidence": ["Minutes represent rapid engagement timelines", "Ultra-short timeframes for critical targets"], "taxonomic_reasoning": "Minutes represent the most compressed engagement timeline category"}, {"id": "hours_timeline", "name": "Hours Timeline", "definition": "Standard engagement requirements measured in hours", "parent_concepts": ["engagement_timelines"], "confidence_score": 0.82, "entity_id": "E-697a211f5b11", "transformation_logs": [{"id": "transform_hours_ti", "stage": "TRANSFORMATION_STAGE_TAXONOMY_GENERATION", "method": "TRANSFORMATION_METHOD_LLM_PROMPT", "from_state": "classified_entities", "to_state": "taxonomy_concepts", "final_confidence": 0.82, "reasoning": "LLM-generated hierarchy with confidence 0.820", "started_at": "2025-12-02T22:12:05.399150Z", "completed_at": "2025-12-02T22:12:05.399160Z", "tenant_id": "default"}], "foundational_ontology_type": "FOUNDATIONAL_ONTOLOGY_TYPE_BFO", "bfo_class": "TemporalRegion", "bfo_uri": "http://purl.obolibrary.org/obo/BFO_0000008", "cco_class": "TemporalInterval", "cco_uri": "http://www.ontologyrepository.com/CommonCoreOntologies/TemporalInterval", "foundational_confidence": 0.9, "semantic_category": "SEMANTIC_CATEGORY_CONCEPT", "source_references": [{"document_id": "batch-ab1be436-e79b-436b-8f65-7bbd5d03d519-c311ea18-6f02-461b-936e-65a4bc6e17c6_chunk_15", "start_index": 3503, "end_index": 3508, "surrounding_text": "focus on complex judgment calls while reducing engagement timelines from hours to minutes.", "extraction_confidence": 0.85, "extraction_method": "LLM_EXTRACTION", "extracted_at": "2025-12-02T22:06:44.666340Z"}], "hierarchical_evidence": ["Hours represent standard engagement timelines", "Longer planning windows for complex operations"], "taxonomic_reasoning": "Hours represent extended but still time-sensitive engagement windows"}, {"id": "ai_ml_tools", "name": "AI/ML Tools", "definition": "Specific artificial intelligence and machine learning software tools and platforms", "parent_concepts": ["ai_ml_automation"], "confidence_score": 0.88, "entity_id": "E-24ba76edd60c", "transformation_logs": [{"id": "transform_ai_ml_to", "stage": "TRANSFORMATION_STAGE_TAXONOMY_GENERATION", "method": "TRANSFORMATION_METHOD_LLM_PROMPT", "from_state": "classified_entities", "to_state": "taxonomy_concepts", "final_confidence": 0.88, "reasoning": "LLM-generated hierarchy with confidence 0.880", "started_at": "2025-12-02T22:12:05.399218Z", "completed_at": "2025-12-02T22:12:05.399223Z", "tenant_id": "default"}], "foundational_ontology_type": "FOUNDATIONAL_ONTOLOGY_TYPE_BFO", 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"SEMANTIC_CATEGORY_CONCEPT", "source_references": [{"document_id": "batch-ab1be436-e79b-436b-8f65-7bbd5d03d519-c311ea18-6f02-461b-936e-65a4bc6e17c6_chunk_15", "start_index": 4096, "end_index": 4115, "surrounding_text": "Train operators and analysts in AI interpretability to ensure trust in outputs.", "extraction_confidence": 0.85, "extraction_method": "LLM_EXTRACTION", "extracted_at": "2025-12-02T22:06:44.668131Z"}], "source_entity_names": ["AI interpretability"], "hierarchical_evidence": ["AI interpretability is critical for AI system trust", "Interpretability enables understanding of AI decisions"], "taxonomic_reasoning": "AI interpretability is essential component of trustworthy AI/ML automation"}, {"id": "confidence_scoring", "name": "Confidence Scoring", "definition": "Quantitative assessment mechanisms for AI/ML system output reliability", "parent_concepts": ["ai_ml_automation"], "confidence_score": 0.84, "entity_id": "E-9bbe990ea58a", "transformation_logs": [{"id": 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"started_at": "2025-12-02T22:12:05.405409Z", "completed_at": "2025-12-02T22:12:05.405415Z", "tenant_id": "default"}], "foundational_ontology_type": "FOUNDATIONAL_ONTOLOGY_TYPE_BFO", "bfo_class": "Process", "bfo_uri": "http://purl.obolibrary.org/obo/BFO_0000015", "cco_class": "SocialAct", "cco_uri": "http://www.ontologyrepository.com/CommonCoreOntologies/SocialAct", "foundational_confidence": 0.88, "semantic_category": "SEMANTIC_CATEGORY_CONCEPT", "source_references": [{"document_id": "batch-ab1be436-e79b-436b-8f65-7bbd5d03d519-c311ea18-6f02-461b-936e-65a4bc6e17c6_chunk_15", "start_index": 3989, "end_index": 4016, "surrounding_text": "Maintain human-in-the-loop oversight for rules of engagement (ROE)", "extraction_confidence": 0.85, "extraction_method": "LLM_EXTRACTION", "extracted_at": "2025-12-02T22:06:44.667621Z"}], "source_entity_names": ["human-in-the-loop oversight"], "hierarchical_evidence": ["Human-in-the-loop ensures human decision authority", "Oversight maintains human control 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