Triple

T22909121
Position Surface form Disambiguated ID Type / Status
Subject Djoum E568536 entity
Predicate regionCapital P16248 FINISHED
Object Ebolowa 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: Ebolowa | Statement: [Djoum, regionCapital, Ebolowa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ebolowa
Context triple: [Djoum, regionCapital, Ebolowa]
  • A. Ebolowa chosen
    Ebolowa is a city in southern Cameroon that serves as an administrative and commercial center for the surrounding agricultural region.
  • B. Ekondo-Titi
    Ekondo-Titi is a coastal town and commune in Cameroon's Southwest Region, known for its agricultural activities and location near the Ndian River and the Atlantic coast.
  • C. Benina
    Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
  • D. Abéché
    Abéché is a major city in eastern Chad that serves as an important regional trade and administrative center.
  • E. Ewondo
    Ewondo is a Bantu language spoken primarily by the Ewondo people in central Cameroon, including in and around the capital city, Yaoundé.
  • 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.