Triple

T8346990
Position Surface form Disambiguated ID Type / Status
Subject A75 motorway E196064 entity
Predicate crossesRegion P13729 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: [A75 motorway, crossesRegion, Auvergne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Auvergne
Context triple: [A75 motorway, crossesRegion, 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_69ca82edd63c8190b876b8465464c5fa completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb8013d81c81908da48466cffb3939 completed March 31, 2026, 8:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69cecc2b3890819091f8947ebf31bad0 completed April 2, 2026, 8:06 p.m.
Created at: March 30, 2026, 5:58 p.m.