Triple

T3853099
Position Surface form Disambiguated ID Type / Status
Subject Auch E85342 entity
Predicate regionCapitalOf P204 FINISHED
Object Gers department E327978 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: Gers department | Statement: [Auch, regionCapitalOf, Gers department]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gers department
Context triple: [Auch, regionCapitalOf, Gers department]
  • A. Gers (department) chosen
    Gers is a rural department in southwestern France, known for its rolling countryside, Armagnac brandy, and traditional Gascon culture.
  • B. Gard department
    Gard department is an administrative region in southern France known for its Mediterranean landscapes, historic Roman sites such as the Pont du Gard, and the city of Nîmes.
  • C. Aude department
    The Aude department is an administrative region in southern France known for its historic towns, vineyards, and proximity to the Mediterranean coast.
  • D. Nord department
    Nord department is an administrative region in northern France bordering Belgium, known for its industrial heritage and historic cities such as Lille.
  • E. Eure department
    The Eure department is an administrative region in northern France’s Normandy known for its rural landscapes, historic towns, and cultural sites such as Claude Monet’s garden at Giverny.
  • 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_69aed936de1c81908f91bed80f70abb2 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeec01f7b48190ba1ec89328b3fccb completed March 9, 2026, 3:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69b51c7b37048190a22907ffc85140c3 completed March 14, 2026, 8:29 a.m.
Created at: March 9, 2026, 3:19 p.m.