Triple

T2358892
Position Surface form Disambiguated ID Type / Status
Subject Rachuonyo District E47223 entity
Predicate majorSettlement P316 FINISHED
Object Kendu Bay E258029 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: Kendu Bay | Statement: [Rachuonyo District, majorSettlement, Kendu Bay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kendu Bay
Context triple: [Rachuonyo District, majorSettlement, Kendu Bay]
  • A. Kendu Bay chosen
    Kendu Bay is a town in western Kenya on the shores of Lake Victoria, known as a local commercial and transport hub for the surrounding region.
  • B. Kivu
    Kivu is a conflict-affected region in eastern Democratic Republic of the Congo known for its rich natural resources, humanitarian crises, and recurrent outbreaks of violence and disease.
  • C. Erg Chigaga
    Erg Chigaga is a vast, remote dune field in southern Morocco known for its towering sand dunes and desert wilderness landscapes.
  • D. Apswa
    Apswa is the endonym used by the Abkhaz people to refer to themselves and their language.
  • E. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • 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_69a88a1a4a6081908645b0f2914521ab completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abc720b9048190a5d3b19e5e1f373a completed March 7, 2026, 6:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69aea88cf3308190bdb5aac38aded823 completed March 9, 2026, 11:01 a.m.
Created at: March 4, 2026, 7:55 p.m.