Triple

T2769252
Position Surface form Disambiguated ID Type / Status
Subject Velay E61412 entity
Predicate partOf P40 FINISHED
Object Auvergne (historical province) E213603 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: Auvergne (historical province) | Statement: [Velay, partOf, Auvergne (historical province)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Auvergne (historical province)
Context triple: [Velay, partOf, Auvergne (historical province)]
  • A. Auvergne chosen
    Auvergne is a historic region in central France known for its volcanic landscapes, rural character, and Romanesque heritage.
  • B. Touraine
    Touraine is a historic region in central France, famed for its Loire Valley châteaux, wine production, and role as a former royal heartland.
  • C. Rouergue
    Rouergue is a historic cultural region in southern France, centered around the present-day Aveyron department and known for its rural landscapes, medieval towns, and Occitan heritage.
  • D. Auvergnat
    Auvergnat is a variety of the Occitan language traditionally spoken in France’s Auvergne region and surrounding areas.
  • E. Auvergne-Rhône-Alpes region
    The Auvergne-Rhône-Alpes region is a large administrative region in east-central France known for its major cities like Lyon and Grenoble, diverse landscapes from the Alps to volcanic highlands, and strong industrial and agricultural economy.
  • 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_69ab4b7cd13481909174bca9809ed259 completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdd6785d88190b99f99889463a962 completed March 7, 2026, 8:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc6496dc88190b316d5b36bc5df67 completed March 10, 2026, 7:20 a.m.
Created at: March 6, 2026, 9:57 p.m.