Triple

T11236053
Position Surface form Disambiguated ID Type / Status
Subject Binoche E265943 entity
Predicate usedBy P260 FINISHED
Object Juliette Binoche E52980 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: Juliette Binoche | Statement: [Binoche, usedBy, Juliette Binoche]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Juliette Binoche
Context triple: [Binoche, usedBy, Juliette Binoche]
  • A. Juliette Binoche chosen
    Juliette Binoche is an acclaimed French actress known for her nuanced performances in international cinema and her Academy Award-winning role in "The English Patient."
  • B. Nathalie Baye
    Nathalie Baye is an acclaimed French actress known for her versatile performances in both art-house and mainstream cinema since the 1970s.
  • C. Béatrice Dalle
    Béatrice Dalle is a French actress known for her intense, unconventional screen presence and breakout role in the 1986 film "Betty Blue."
  • D. Nelly Auteuil
    Nelly Auteuil is the daughter of French actor and filmmaker Daniel Auteuil.
  • E. Sandrine Bonnaire
    Sandrine Bonnaire is an acclaimed French actress and filmmaker known for her powerful performances in films such as "Vagabond" and "Under the Sun of Satan."
  • 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_69d6aac656d48190b275efaa7d6074ee completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e904cf888190826fc964f76b5cb2 completed April 9, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69e509ea915481909c41a4a89ae6ee80 completed April 19, 2026, 4:59 p.m.
Created at: April 8, 2026, 9:30 p.m.