Triple

T3123173
Position Surface form Disambiguated ID Type / Status
Subject Kogi State E65233 entity
Predicate createdFrom P5574 FINISHED
Object Kwara State E64885 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: Kwara State | Statement: [Kogi State, createdFrom, Kwara State]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kwara State
Context triple: [Kogi State, createdFrom, Kwara State]
  • A. Kwara State chosen
    Kwara State is a north-central Nigerian state with a significant Yoruba population and a cultural blend of northern and southwestern Nigerian influences.
  • B. Ondo State
    Ondo State is a coastal state in southwestern Nigeria known for its oil-producing areas, diverse ethnic communities, and significant role within the Niger Delta region.
  • C. Kaduna State
    Kaduna State is a major administrative and economic state in northwestern Nigeria, known for its diverse population, educational institutions, and role as a political and industrial hub in the region.
  • D. Ogun State
    Ogun State is a southwestern Nigerian state known as a key Yoruba cultural heartland and an important industrial and educational hub.
  • E. Ekiti State
    Ekiti State is a landlocked, predominantly Yoruba-speaking state in southwestern Nigeria known for its hilly terrain and strong emphasis on education.
  • 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_69ad8580c72481909672d37acf647893 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada52c105c8190b8128e66d9b9e8a0 completed March 8, 2026, 4:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf832772d08190842b1e1c1a87ea3a completed March 22, 2026, 5:50 a.m.
Created at: March 8, 2026, 3:04 p.m.