Triple

T10490529
Position Surface form Disambiguated ID Type / Status
Subject Luo language E247405 entity
Predicate hasDialects P4251 FINISHED
Object Central Nyanza Luo 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: Central Nyanza Luo | Statement: [Luo language, hasDialects, Central Nyanza Luo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Central Nyanza Luo
Context triple: [Luo language, hasDialects, Central Nyanza Luo]
  • 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. 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.
  • C. Singida Region
    Singida Region is an administrative region in central Tanzania known for its semi-arid climate, agriculture, and role as a transport crossroads.
  • D. Nyanga Province
    Nyanga Province is a sparsely populated, resource-rich administrative region in southern Gabon known for its forests, rivers, and coastal areas along the Atlantic Ocean.
  • E. Simiyu Region
    Simiyu Region is an administrative region in northern Tanzania known for its predominantly rural economy based on agriculture and livestock.
  • 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_69d381c309b88190af78aa681cf6a4c2 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d5097d61e08190952d4354ef1bce52 completed April 7, 2026, 1:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69d90dd040f48190a645ebd131f9205c completed April 10, 2026, 2:48 p.m.
Created at: April 6, 2026, 12:23 p.m.