Triple

T2358866
Position Surface form Disambiguated ID Type / Status
Subject Luo people E47222 entity
Predicate region P40 FINISHED
Object Nyanza region E237667 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: Nyanza region | Statement: [Luo people, region, Nyanza region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nyanza region
Context triple: [Luo people, region, Nyanza region]
  • A. Nyanza region chosen
    Nyanza region is an area in western Kenya along Lake Victoria, known for its predominantly Luo population and the city of Kisumu as its main urban center.
  • B. Mwanza Region
    Mwanza Region is an administrative region in northwestern Tanzania, located along the southern shores of Lake Victoria and known as a major economic and cultural center, including for the Sukuma people.
  • C. Simiyu Region
    Simiyu Region is an administrative region in northern Tanzania known for its predominantly rural economy based on agriculture and livestock.
  • D. Kagera Region
    Kagera Region is a northwestern region of Tanzania bordering Lake Victoria and several East African countries, known for its diverse ethnic groups, agriculture, and historical significance.
  • E. Singida Region
    Singida Region is an administrative region in central Tanzania known for its semi-arid climate, agriculture, and role as a transport crossroads.
  • 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_69aebf2eb7a88190b421df42acbd974b completed March 9, 2026, 12:38 p.m.
Created at: March 4, 2026, 7:55 p.m.