Triple

T23368459
Position Surface form Disambiguated ID Type / Status
Subject Oroko E593391 entity
Predicate region P40 FINISHED
Object Fako Division 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: Fako Division | Statement: [Oroko, region, Fako Division]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fako Division
Context triple: [Oroko, region, Fako Division]
  • A. Fako Division chosen
    Fako Division is an administrative division in Cameroon’s Southwest Region that includes coastal towns such as Limbe and the slopes of Mount Cameroon.
  • B. Koulamoutou
    Koulamoutou is a town in central Gabon that serves as an administrative and economic hub for the surrounding region.
  • C. Rumuokoro
    Rumuokoro is a bustling urban town and major commercial transport hub in Obio-Akpor, within the Port Harcourt metropolitan area of Rivers State, Nigeria.
  • D. Fako
    Fako is an alternative name for Mount Cameroon, an active volcano and the highest mountain in West and Central Africa located in southwestern Cameroon.
  • E. Khondji
    Khondji is the surname of Darius Khondji, a renowned cinematographer known for his visually distinctive work on international films.
  • 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_69e25d2593c88190bcdf4a716a94ccb2 completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f1a0aed374819097d38f51894bee44 completed April 29, 2026, 6:09 a.m.
Created at: April 17, 2026, 5:32 p.m.