Triple

T1776598
Position Surface form Disambiguated ID Type / Status
Subject DICOM standard E38992 entity
Predicate relatedStandard P37 FINISHED
Object HL7 E6183 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: HL7 | Statement: [DICOM standard, relatedStandard, HL7]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: HL7
Context triple: [DICOM standard, relatedStandard, HL7]
  • A. HL7 standards chosen
    HL7 standards are a widely adopted set of international specifications for the exchange, integration, sharing, and retrieval of electronic health information between healthcare systems.
  • B. SNOMED CT
    SNOMED CT is a comprehensive, multilingual clinical healthcare terminology used worldwide to standardize the recording and sharing of medical information in electronic health records.
  • C. LOINC
    LOINC (Logical Observation Identifiers Names and Codes) is a widely used international standard for identifying laboratory tests, clinical measurements, and other health observations in electronic health records and data exchange.
  • D. SNOMED International
    SNOMED International is a global not-for-profit organization responsible for developing and promoting the SNOMED CT clinical terminology standard used in healthcare systems worldwide.
  • E. MEDITECH
    MEDITECH is a healthcare information technology company best known for providing electronic health record (EHR) and hospital information systems to healthcare organizations.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69a8862e61708190af97b9838cc3f5de completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa64b839608190b32bc041267458d5 completed March 6, 2026, 5:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada99a81c08190b602858708263193 completed March 8, 2026, 4:53 p.m.
Created at: March 4, 2026, 7:31 p.m.