Triple

T17056184
Position Surface form Disambiguated ID Type / Status
Subject Dakar Department E413825 entity
Predicate contains P35 FINISHED
Object Rufisque Department E418928 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: Rufisque Department | Statement: [Dakar Department, contains, Rufisque Department]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rufisque Department
Context triple: [Dakar Department, contains, Rufisque Department]
  • A. Rufisque Department chosen
    Rufisque Department is an administrative subdivision of Senegal located within the Dakar Region, encompassing both urban and peri-urban communities east of the capital.
  • B. Lékoumou Department
    Lékoumou Department is an administrative region in the Republic of the Congo located in the southern part of the country.
  • C. Diffa Department
    Diffa Department is an administrative subdivision in southeastern Niger that encompasses the city of Diffa and surrounding areas near the border with Nigeria and Chad.
  • D. Offoué-Onoy Department
    Offoué-Onoy Department is an administrative division in eastern Gabon, situated within Ogooué-Lolo Province.
  • E. Podor Department
    Podor Department is an administrative division in northern Senegal, situated along the Senegal River and known for its historic towns and agricultural communities.
  • 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_69d886cde3d481908d4d01ba88ba7eb7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3db7934e881909e9f0ae956fb0816 completed April 18, 2026, 7:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a014823d0a8819095491aef9b258971 completed May 11, 2026, 3:08 a.m.
Created at: April 10, 2026, 5:34 a.m.