Triple

T9395130
Position Surface form Disambiguated ID Type / Status
Subject Isabelle Huppert E226123 entity
Predicate notableWork P4 FINISHED
Object Violette Nozière E421300 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: Violette Nozière | Statement: [Isabelle Huppert, notableWork, Violette Nozière]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Violette Nozière
Context triple: [Isabelle Huppert, notableWork, Violette Nozière]
  • A. Violette Nozière chosen
    Violette Nozière is a 1978 French crime drama film by Claude Chabrol that recounts the true story of a notorious 1930s French parricide case.
  • B. Mireille Darc
    Mireille Darc was a prominent French actress and model, best known for her roles in 1960s–1970s French cinema and her collaborations with director Georges Lautner.
  • C. Arletty
    Arletty was a celebrated French actress and singer, renowned for her witty, worldly screen presence in classic 1930s and 1940s cinema.
  • D. Marie Trintignant
    Marie Trintignant was a French actress known for her intense, emotionally charged performances in film, television, and theater before her life was tragically cut short.
  • E. Micheline Presle
    Micheline Presle is a renowned French actress known for her prolific film and television career spanning from the 1940s onward.
  • 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_69ca842f7e3481908bf5bcf52e032dbd completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd5112faa08190a8dd2c461d1e7a14 completed April 1, 2026, 5:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69d110214f088190a972c8534b613873 completed April 4, 2026, 1:20 p.m.
Created at: March 30, 2026, 7:45 p.m.