Triple

T18225218
Position Surface form Disambiguated ID Type / Status
Subject Jacques Necker E436403 entity
Predicate residence P75 FINISHED
Object Coppet 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: Coppet | Statement: [Jacques Necker, residence, Coppet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Coppet
Context triple: [Jacques Necker, residence, Coppet]
  • A. Coppet chosen
    Coppet is a Swiss lakeside town on Lake Geneva in the canton of Vaud, known for its historic château and role as a regional transport hub.
  • B. Villars-le-Comte
    Villars-le-Comte is a small rural municipality in the canton of Vaud in western Switzerland.
  • C. Cluses
    Cluses is a small industrial town in southeastern France known for its precision engineering and watchmaking heritage, located in the Arve Valley of the Haute-Savoie department in the Alps.
  • D. Nyons
    Nyons is a small town in southeastern France renowned for its olive production and picturesque setting in the Drôme Provençale region.
  • E. Saignelégier
    Saignelégier is a municipality in the Swiss canton of Jura known for its rural landscapes, watchmaking heritage, and the annual Marché-Concours horse festival.
  • 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_69d8b9103a8081908bbb0836fef10efd completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4f4aeb4cc81908959413c368a2243 completed April 19, 2026, 3:28 p.m.
Created at: April 10, 2026, 10:32 a.m.