Triple

T13538085
Position Surface form Disambiguated ID Type / Status
Subject D1 Arkema E323312 entity
Predicate formerName P65 FINISHED
Object Division 1 Féminine E62057 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: Division 1 Féminine | Statement: [D1 Arkema, formerName, Division 1 Féminine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Division 1 Féminine
Context triple: [D1 Arkema, formerName, Division 1 Féminine]
  • A. Division 1 Féminine chosen
    Division 1 Féminine is the top-tier professional women's football league in France, featuring the country's strongest clubs and many of the world's elite players.
  • B. Division 2 Féminine
    Division 2 Féminine is the second tier of women's football in France, sitting below the top-flight Division 1 Féminine in the national league system.
  • C. Championnat National 2
    Championnat National 2 is the fourth tier of the French football league system, featuring semi-professional and amateur clubs from across France.
  • D. Open de France féminin
    Open de France féminin is a professional women's golf tournament held in France and part of the Ladies European Tour.
  • E. Ligue 2
    Ligue 2 is the second tier of professional football in the French league system, sitting directly below Ligue 1.
  • 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_69d8076776248190bdf0d4fa1f85a5fc completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbafbe39948190808062d4eff91841 completed April 12, 2026, 2:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69f75d9c04b881908a359df791b89b43 completed May 3, 2026, 2:37 p.m.
Created at: April 9, 2026, 9:45 p.m.