Triple

T1464060
Position Surface form Disambiguated ID Type / Status
Subject Nièvre E31578 entity
Predicate administrativeCenter P1474 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: [Nièvre, administrativeCenter, Nevers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nevers
Context triple: [Nièvre, administrativeCenter, Nevers]
  • A. Nevers chosen
    Nevers is a historic city in central France known for its medieval architecture, religious heritage, and traditional faience pottery.
  • B. Boncourt
    Boncourt is a locality known for its historic Château de Boncourt, reflecting its cultural and architectural heritage.
  • C. Roanne
    Roanne is a commune and industrial town in central France, situated on the Loire River and known historically for its textile industry and river port.
  • D. Choulex
    Choulex is a small municipality in the canton of Geneva in southwestern Switzerland, known for its rural character and proximity to the city of Geneva.
  • E. Compiegne
    Compiègne is a historic city in northern France known for its royal château, forest, and role in significant events such as the signing of the 1918 Armistice.
  • 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_69a49917dfc081909acdbdf5d684f1ef completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c5b89708819084fb9ba4ff293b8b completed March 1, 2026, 11:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad308a2c448190b78cee5506c02e49 completed March 8, 2026, 8:17 a.m.
Created at: March 1, 2026, 8 p.m.