Triple

T16889167
Position Surface form Disambiguated ID Type / Status
Subject Route nationale 106 E421618 entity
Predicate traverses P416 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: [Route nationale 106, traverses, Massif Central region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Massif Central region
Context triple: [Route nationale 106, traverses, 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_69d889d470fc8190b4aec199636c0c56 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e3bbc3b5188190ac713b4d4166e961 completed April 18, 2026, 5:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00cfca11bc8190b0835de0d56ca0b1 completed May 10, 2026, 6:34 p.m.
Created at: April 10, 2026, 5:29 a.m.