Triple

T8996426
Position Surface form Disambiguated ID Type / Status
Subject Bourgogne-Franche-Comté E214924 entity
Predicate containsCity P294 FINISHED
Object Nevers E172114 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: Nevers | Statement: [Bourgogne-Franche-Comté, containsCity, Nevers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nevers
Context triple: [Bourgogne-Franche-Comté, containsCity, Nevers]
  • A. Nevers chosen
    Nevers is a historic city in central France known for its medieval architecture, religious heritage, and traditional faience pottery.
  • B. Toul
    Toul is a historic commune in northeastern France known for its medieval fortifications and impressive Gothic cathedral.
  • C. 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.
  • D. Villeurbannais
    Villeurbannais is the French term for an inhabitant or native of the city of Villeurbanne, located near Lyon in eastern France.
  • E. Roannais
    Roannais is a natural region in central France known for its rolling countryside, agricultural landscapes, and proximity to the upper Loire River.
  • 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_69ca83a05c608190bdfdbdb25e994b39 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc68df33c48190a5017426e59c0bc4 completed April 1, 2026, 12:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfd0d586c881909090b424f6fd036f completed April 3, 2026, 2:38 p.m.
Created at: March 30, 2026, 7:04 p.m.