Triple

T2761685
Position Surface form Disambiguated ID Type / Status
Subject Girondins de Bordeaux E61233 entity
Predicate owner P347 FINISHED
Object Gérard Lopez E301334 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: Gérard Lopez | Statement: [Girondins de Bordeaux, owner, Gérard Lopez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gérard Lopez
Context triple: [Girondins de Bordeaux, owner, Gérard Lopez]
  • A. Gérard Lopez chosen
    Gérard Lopez is a Luxembourgish-Spanish businessman and investor known for owning and leading several European football clubs, including Girondins de Bordeaux and previously Lille OSC.
  • B. Michel Andrault
    Michel Andrault was a prominent French architect known for his influential large-scale housing and urban development projects in the late 20th century.
  • C. Gérard de Battista
    Gérard de Battista is a French cinematographer known for his work on numerous European films, including the acclaimed drama "Monsieur Ibrahim."
  • D. Jean-Claude Olivier
    Jean-Claude Olivier is a writer associated with the Juicy brand or publication.
  • E. Alain Glavieux
    Alain Glavieux was a French engineer and information theorist best known as a co-inventor of turbo codes, a breakthrough in error-correcting coding that revolutionized digital communications.
  • 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_69ab4b7bab6c8190a5c2efef19a8ef34 completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdd5072548190946f037c38aabb02 completed March 7, 2026, 8:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69afe89b94848190946abef7216339be completed March 10, 2026, 9:47 a.m.
Created at: March 6, 2026, 9:57 p.m.