Triple

T13631113
Position Surface form Disambiguated ID Type / Status
Subject Egun E325718 entity
Predicate region P40 FINISHED
Object Ouémé Department E689810 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: Ouémé Department | Statement: [Egun, region, Ouémé Department]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ouémé Department
Context triple: [Egun, region, Ouémé Department]
  • A. Ouémé Department chosen
    Ouémé Department is an administrative region in southeastern Benin that includes the national capital, Porto-Novo, and is known for its role as a political and economic hub.
  • B. Wouri Department
    Wouri Department is an administrative division in Cameroon that encompasses the major economic hub and port city of Douala.
  • C. Ouest Department
    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.
  • D. Lopé Department
    Lopé Department is an administrative division in central Gabon known for encompassing parts of the ecologically rich Lopé National Park.
  • E. 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.
  • 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_69d8076beddc8190a53156f5bea77f5e completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbbe9ea9088190a17270dec82bbcaa completed April 12, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69f77fab07648190b3b3362a8ffa8961 completed May 3, 2026, 5:02 p.m.
Created at: April 9, 2026, 9:51 p.m.