Triple

T9389362
Position Surface form Disambiguated ID Type / Status
Subject Canal du Nivernais E225984 entity
Predicate region P40 FINISHED
Object Nivernais E150661 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: Nivernais | Statement: [Canal du Nivernais, region, Nivernais]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nivernais
Context triple: [Canal du Nivernais, region, Nivernais]
  • A. Nivernais chosen
    Nivernais is a historic province in central France, centered around the town of Nevers and known for its rural landscapes and traditional agriculture.
  • B. Tournaisis
    Tournaisis is a historical region in present-day Belgium centered around the city of Tournai, known for its medieval political significance and rich cultural heritage.
  • C. Vendômois
    Vendômois is the French demonym referring to inhabitants or natives of the town of Vendôme in central France.
  • D. Vosgien
    Vosgien is a regional dialect of the Lorrain language spoken in the Vosges area of northeastern France.
  • E. Montévrain
    Montévrain is a suburban commune in the eastern outskirts of Paris, France, known for its proximity to Disneyland Paris and its role in the Marne-la-Vallée new town development.
  • 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_69ca842f7e3481908bf5bcf52e032dbd completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd50d83c188190925c389dff1341b0 completed April 1, 2026, 5:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69d100f86bac8190a26ab156f24455bb completed April 4, 2026, 12:15 p.m.
Created at: March 30, 2026, 7:45 p.m.