Triple

T10070319
Position Surface form Disambiguated ID Type / Status
Subject Auvergne E213603 entity
Predicate contains P35 FINISHED
Object Issoire E103788 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: Issoire | Statement: [Auvergne, contains, Issoire]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Issoire
Context triple: [Auvergne, contains, Issoire]
  • A. Issoire chosen
    Issoire is a historic town in central France’s Auvergne region, known for its Romanesque architecture and location in the valley of the Allier River.
  • B. Tatihou
    Tatihou is a small French island off the coast of Normandy known for its historic Vauban fortifications, maritime museum, and rich coastal birdlife.
  • C. Patouès
    Patouès is a regional Romance dialect of the Franco-Provençal language traditionally spoken in parts of France, Switzerland, and Italy.
  • D. Mouriès
    Mouriès is a village in southern France’s Provence region, known for its olive oil production and location near the Alpilles hills.
  • E. Saussignac
    Saussignac is a small wine-producing commune in southwestern France, known for its sweet white wines made primarily from Sémillon and other Bordeaux grape varieties.
  • 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_69ca839add308190b57d53b4ec21f2d0 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdcffa045c8190a08db0bb74cb006a completed April 2, 2026, 2:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69d29aa05cc881909f59178e9c6c01ef completed April 5, 2026, 5:23 p.m.
Created at: March 30, 2026, 8:58 p.m.