Triple

T4207134
Position Surface form Disambiguated ID Type / Status
Subject Claude Chabrol E93809 entity
Predicate notableWork P4 FINISHED
Object Que la bête meure E359787 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: Que la bête meure | Statement: [Claude Chabrol, notableWork, Que la bête meure]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Que la bête meure
Context triple: [Claude Chabrol, notableWork, Que la bête meure]
  • A. Le Blaireau
    Le Blaireau is the famous nickname of Bernard Hinault, the legendary French cyclist and multiple Tour de France winner.
  • B. L'odeur des fauves
    L'odeur des fauves is a film featuring French-American actress Josephine Chaplin, known for her work in European cinema.
  • C. The Dying Animal chosen
    The Dying Animal is a short novel by Philip Roth that explores aging, desire, and mortality through the obsessive relationship of an aging cultural critic with a much younger woman.
  • D. D’entre les morts
    D’entre les morts is a 1954 French crime novel by Pierre Boileau and Thomas Narcejac that served as the literary basis for Alfred Hitchcock’s film Vertigo.
  • E. Terret Noir
    Terret Noir is a light-colored, relatively rare French wine grape variety traditionally used in southern Rhône and Languedoc blends for its freshness and acidity.
  • 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_69b3451743608190808f41d17ccf2650 completed March 12, 2026, 10:58 p.m.
NER Named-entity recognition batch_69b3480cfacc81909a2705eb4e9ce8c1 completed March 12, 2026, 11:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69b59628da008190ae0e458ed3a5890b completed March 14, 2026, 5:08 p.m.
Created at: March 12, 2026, 11:03 p.m.