| source_references |
[{"documentId": "batch-31c0285e-ef14-4 [{"documentId": "batch-31c0285e-ef14-4c54-807e-7adeffb0dff2-356a3df1-e4e5-4c29-b3a6-ef32fbf3751d_chunk_6", "startIndex": 1717, "endIndex": 1726, "surroundingText": "\uf0b7 Visible \u2013 Consumers can locate the needed data.", "extractionConfidence": 0.85, "extractionMethod": "LLM_EXTRACTION", "extractedAt": "2025-12-03T15:24:27.705100Z"}, {"documentId": "batch-31c0285e-ef14-4c54-807e-7adeffb0dff2-356a3df1-e4e5-4c29-b3a6-ef32fbf3751d_chunk_6", "startIndex": 1771, "endIndex": 1780, "surroundingText": "\uf0b7 Accessible \u2013 Consumers can retrieve the data.", "extractionConfidence": 0.85, "extractionMethod": "LLM_EXTRACTION", "extractedAt": "2025-12-03T15:24:27.705134Z"}, {"documentId": "batch-31c0285e-ef14-4c54-807e-7adeffb0dff2-356a3df1-e4e5-4c29-b3a6-ef32fbf3751d_chunk_6", "startIndex": 1824, "endIndex": 1833, "surroundingText": "\uf0b7 Understandable \u2013 Consumers can find descriptions of data to recognize the content,", "extractionConfidence": 0.85, "extractionMethod": "LLM_EXTRACTION", "extractedAt": "2025-12-03T15:24:27.705169Z"}, {"documentId": "batch-31c0285e-ef14-4c54-807e-7adeffb0dff2-356a3df1-e4e5-4c29-b3a6-ef32fbf3751d_chunk_6", "startIndex": 1931, "endIndex": 1940, "surroundingText": "\uf0b7 Linked \u2013 Consumers can exploit complementary data elements through innate", "extractionConfidence": 0.85, "extractionMethod": "LLM_EXTRACTION", "extractedAt": "2025-12-03T15:24:27.705207Z"}, {"documentId": "batch-31c0285e-ef14-4c54-807e-7adeffb0dff2-356a3df1-e4e5-4c29-b3a6-ef32fbf3751d_chunk_6", "startIndex": 2029, "endIndex": 2038, "surroundingText": "\uf0b7 Trustworthy \u2013 Consumers can be confident in all aspects of data for decision-making.", "extractionConfidence": 0.85, "extractionMethod": "LLM_EXTRACTION", "extractedAt": "2025-12-03T15:24:27.705239Z"}]... |
| transformation_logs |
[{"id": "entity_transform_E-41341a9d63 [{"id": "entity_transform_E-41341a9d633d", "stage": "TRANSFORMATION_STAGE_ENTITY_EXTRACTION", "method": "TRANSFORMATION_METHOD_LLM_PROMPT", "fromState": "raw_document_text", "toState": "extracted_entities", "finalConfidence": 0.85, "reasoning": "LLM-based entity extraction from document batch-31c0285e-ef14-4c54-807e-7adeffb0dff2-356a3df1-e4e5-4c29-b3a6-ef32fbf3751d_chunk_6 using structured JSON schema", "modelName": "LLM_CLIENT", "startedAt": "2025-12-03T15:24:27.694380Z", "completedAt": "2025-12-03T15:24:27.694412Z"}]... |