Triple

T6944258
Position Surface form Disambiguated ID Type / Status
Subject Brigitte Marie-Claude Trogneux E160755 entity
Predicate name P16 FINISHED
Object Brigitte Marie-Claude Trogneux E160755 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: Brigitte Marie-Claude Trogneux | Statement: [Brigitte Marie-Claude Trogneux, name, Brigitte Marie-Claude Trogneux]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Brigitte Marie-Claude Trogneux
Context triple: [Brigitte Marie-Claude Trogneux, name, Brigitte Marie-Claude Trogneux]
  • A. Brigitte Marie-Claude Trogneux chosen
    Brigitte Marie-Claude Trogneux is a French former teacher and the First Lady of France, married to President Emmanuel Macron.
  • B. Claudie Haigneré
    Claudie Haigneré is a French physician, astronaut, and former government minister who became the first French woman in space and a prominent figure in European space exploration.
  • C. Nelly Auteuil
    Nelly Auteuil is the daughter of French actor and filmmaker Daniel Auteuil.
  • D. Melanie Thierry
    Melanie Thierry is a French actress and former model known for her roles in both European cinema and international films.
  • E. Sandrine Kiberlain
    Sandrine Kiberlain is a French actress and singer known for her acclaimed performances in both dramatic and comedic films.
  • 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_69c6884f3db4819080ad65da69386206 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6da88b79c8190a8f297dfc4972979 completed March 27, 2026, 7:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69c75866a2408190b472fdad73a8799b completed March 28, 2026, 4:26 a.m.
Created at: March 27, 2026, 2:28 p.m.