Triple

T14813638
Position Surface form Disambiguated ID Type / Status
Subject Cère E348249 entity
Predicate geographicLocation P40 FINISHED
Object Massif Central region E9424 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: Massif Central region | Statement: [Cère, geographicLocation, Massif Central region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Massif Central region
Context triple: [Cère, geographicLocation, Massif Central region]
  • A. Massif Central chosen
    The Massif Central is a vast highland region in south-central France characterized by ancient volcanic mountains, plateaus, and deep river valleys.
  • B. La Région Centrale
    La Région Centrale is an experimental 1971 Canadian film by Michael Snow, renowned for its abstract, machine-controlled camera movements in a remote landscape.
  • C. Auvergne
    Auvergne is a historic region in central France known for its volcanic landscapes, rural character, and Romanesque heritage.
  • D. Grands-Ponts Region
    Grands-Ponts Region is an administrative region in southern Ivory Coast known for its coastal location and inclusion within the larger Lagunes District.
  • 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_69d822eb8f588190bf53445e730a934f completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69decfe0e89c81908c0e1fe2bc3ebcfc completed April 14, 2026, 11:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe24cca0b88190b20a6c7dd77b8146 completed May 8, 2026, 6 p.m.
Created at: April 10, 2026, 1:48 a.m.