Triple

T21234748
Position Surface form Disambiguated ID Type / Status
Subject Anaxagoras Chaumette E523313 entity
Predicate birthPlace P1 FINISHED
Object Nevers 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: Nevers | Statement: [Anaxagoras Chaumette, birthPlace, Nevers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nevers
Context triple: [Anaxagoras Chaumette, birthPlace, Nevers]
  • A. Nevers chosen
    Nevers is a historic city in central France known for its medieval architecture, religious heritage, and traditional faience pottery.
  • B. Villeblevin
    Villeblevin is a small commune in north-central France, best known as the place where writer Albert Camus died in a car accident.
  • C. Toul
    Toul is a historic commune in northeastern France known for its medieval fortifications and impressive Gothic cathedral.
  • D. Noville
    Noville is a small Swiss municipality in the canton of Vaud, situated near the eastern end of Lake Geneva and known for its natural wetlands and rural character.
  • E. Villeurbannais
    Villeurbannais is the French term for an inhabitant or native of the city of Villeurbanne, located near Lyon in eastern France.
  • 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_69e0b512ad94819087942b2ed925185f completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e7351f0e988190bb77b598cb8a9532 completed April 21, 2026, 8:28 a.m.
Created at: April 16, 2026, 3:46 p.m.