Triple

T18693730
Position Surface form Disambiguated ID Type / Status
Subject DIMDI E457062 entity
Predicate shortName P43 FINISHED
Object DIMDI NE NERFINISHED

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: DIMDI | Statement: [DIMDI, shortName, DIMDI]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: DIMDI
Context triple: [DIMDI, shortName, DIMDI]
  • A. DIMDI chosen
    DIMDI is a former German federal institute responsible for medical classification systems, health information, and the provision of medical databases and terminology standards.
  • B. DIM
    DIM is the commonly used abbreviation for Deportivo Independiente Medellín, a professional football club based in Medellín, Colombia.
  • C. DGMI
    DGMI is the abbreviated title for the Director General of Military Intelligence, the senior official overseeing military intelligence operations and analysis.
  • D. CIDI
    CIDI is a specialized body of the Organization of American States that promotes integral development and cooperation among its member states in the Americas.
  • E. IDFM
    IDFM is the abbreviation for Île-de-France Mobilités, the public authority that organizes and coordinates public transportation in the Paris metropolitan region.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8d391eb488190ac2e9abf5bf255e4 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e562e581f48190997d279296a31e7f completed April 19, 2026, 11:19 p.m.
Created at: April 10, 2026, 11:49 a.m.