Triple

T7223921
Position Surface form Disambiguated ID Type / Status
Subject Cabaret E150328 entity
Predicate administrativeDivisionOf P747 FINISHED
Object Ouest Department E28121 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: Ouest Department | Statement: [Cabaret, administrativeDivisionOf, Ouest Department]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ouest Department
Context triple: [Cabaret, administrativeDivisionOf, Ouest Department]
  • A. Ouest Department chosen
    Ouest Department is an administrative region in western Haiti that includes the capital city, Port-au-Prince, and serves as the country’s political and economic center.
  • B. Wouri Department
    Wouri Department is an administrative division in Cameroon that encompasses the major economic hub and port city of Douala.
  • C. Sud Department
    Sud Department is an administrative region in southern Haiti known for its coastal cities, beaches, and agricultural activities.
  • D. Beni Department
    Beni Department is a large, sparsely populated administrative region in northern Bolivia known for its vast Amazonian lowlands, wetlands, and cattle ranching.
  • E. Rufisque Department
    Rufisque Department is an administrative subdivision of Senegal located within the Dakar Region, encompassing both urban and peri-urban communities east of the capital.
  • 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_69c687effb44819092b95d07d0368c9f completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6e9db51888190b8463d0003f334fa completed March 27, 2026, 8:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8a1fd9ecc8190a6a136778422f264 completed March 29, 2026, 3:52 a.m.
Created at: March 27, 2026, 2:54 p.m.