Triple

T20061215
Position Surface form Disambiguated ID Type / Status
Subject CNP Assurances E499477 entity
Predicate hasBoardRepresentationFrom P120393 FINISHED
Object La Banque Postale NE NERFINISHED

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: La Banque Postale | Statement: [CNP Assurances, hasBoardRepresentationFrom, La Banque Postale]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: La Banque Postale
Context triple: [CNP Assurances, hasBoardRepresentationFrom, La Banque Postale]
  • A. La Banque Postale chosen
    La Banque Postale is a major French public bank, created from the postal services, that offers retail banking, insurance, and financial services to individuals and businesses.
  • B. Les Bouchères
    Les Bouchères is a named vineyard climat in the Meursault appellation of Burgundy, France, known for producing high-quality Chardonnay wines.
  • C. Banque de Bordeaux
    Banque de Bordeaux was a regional French bank based in Bordeaux that played a role in the development of local finance and commerce.
  • D. Le Million
    Le Million is a 1931 French musical comedy film directed by René Clair, celebrated for its inventive use of sound and playful, surreal style.
  • E. Banque de l’Oise
    Banque de l’Oise was a regional French bank that played a role in local finance and industry development in the Oise department.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da6276bcf48190aabbf279192a5fb4 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e6637601dc8190a07fc20844093cb7 completed April 20, 2026, 5:33 p.m.
Created at: April 11, 2026, 3:38 p.m.