Triple

T12320193
Position Surface form Disambiguated ID Type / Status
Subject Rocher Saint-Michel d’Aiguilhe E293706 entity
Predicate region P40 FINISHED
Object Auvergne 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 | Statement: [Rocher Saint-Michel d’Aiguilhe, region, Auvergne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Auvergne
Context triple: [Rocher Saint-Michel d’Aiguilhe, region, Auvergne]
  • A. Auvergne chosen
    Auvergne is a historic region in central France known for its volcanic landscapes, rural character, and Romanesque heritage.
  • B. Auvergnat
    Auvergnat is a variety of the Occitan language traditionally spoken in France’s Auvergne region and surrounding areas.
  • C. 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.
  • D. Massif Central
    The Massif Central is a vast highland region in south-central France characterized by ancient volcanic mountains, plateaus, and deep river valleys.
  • E. Cévennes
    The Cévennes is a rugged mountainous region in south-central France known for its dramatic landscapes, chestnut forests, and historical role as a refuge for Protestant Huguenots.
  • 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_69d6ab6ae0dc8190b1522a9c1c55c114 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f4c2b548190938fff9427f07dc7 completed April 10, 2026, 6:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f634515d20819094c9bc4f2c7cda8a completed May 2, 2026, 5:28 p.m.
Created at: April 8, 2026, 9:53 p.m.