Triple

T9488789
Position Surface form Disambiguated ID Type / Status
Subject Nevers railway station E228828 entity
Predicate serves P98 FINISHED
Object city of 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: city of Nevers | Statement: [Nevers railway station, serves, city of Nevers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: city of Nevers
Context triple: [Nevers railway station, serves, city of Nevers]
  • A. Nevers chosen
    Nevers is a historic city in central France known for its medieval architecture, religious heritage, and traditional faience pottery.
  • B. city of Tours
    The city of Tours is a historic city in central France, known for its medieval old town, role as a gateway to the Loire Valley châteaux, and rich cultural heritage.
  • C. Tournus
    Tournus is a historic town in eastern France’s Burgundy region, known for its Romanesque abbey and riverside setting along the Saône.
  • D. Ville de Moulins
    Ville de Moulins is a French municipal authority that administers the town of Moulins in central France, including cultural sites such as Maison Mantin.
  • E. 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.
  • 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_69ca847424f081908180305555139f7a completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd80c5a05c8190b97d34f010e60ca1 completed April 1, 2026, 8:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69d12d18fd908190b562fa0a8dad7c63 completed April 4, 2026, 3:24 p.m.
Created at: March 30, 2026, 7:55 p.m.