Triple

T8720106
Position Surface form Disambiguated ID Type / Status
Subject TER Bourgogne-Franche-Comté E206988 entity
Predicate hasStationInCity P78960 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: [TER Bourgogne-Franche-Comté, hasStationInCity, Nevers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nevers
Context triple: [TER Bourgogne-Franche-Comté, hasStationInCity, 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_69ca835811d8819081ea00fd2a2c9a1c completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5d02a52c81909f93622ae6920b80 completed March 31, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf28f599a481908e93bc5b5c41296e completed April 3, 2026, 2:41 a.m.
Created at: March 30, 2026, 6:36 p.m.