Triple

T22908992
Position Surface form Disambiguated ID Type / Status
Subject Kribi E568530 entity
Predicate regionCapitalOf P204 FINISHED
Object Océan Department NE NERFINISHED

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: Océan Department | Statement: [Kribi, regionCapitalOf, Océan Department]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Océan Department
Context triple: [Kribi, regionCapitalOf, Océan Department]
  • A. Océan Department chosen
    Océan Department is an administrative division in Cameroon's South Region, known for its Atlantic coastline, port towns, and diverse coastal ecosystems.
  • B. Atlantique Department
    Atlantique Department is an administrative region in southern Benin that includes coastal and historically significant cities such as Ouidah.
  • C. Saint-Louis Department
    Saint-Louis Department is an administrative division in northern Senegal that encompasses the historic city of Saint-Louis and its surrounding areas.
  • D. Borgou Department
    Borgou Department is an administrative region in northeastern Benin known for its diverse ethnic communities, agriculture-based economy, and the major city of Parakou.
  • E. Plateaux Department
    Plateaux Department is an administrative region in the Republic of the Congo located in the central part of the country, known for its savanna landscapes and relatively low population density.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e2458cd9e48190943ad2e34485d939 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f18072986881909eefb317ced5a145 completed April 29, 2026, 3:52 a.m.
Created at: April 17, 2026, 3:42 p.m.