Triple

T537895
Position Surface form Disambiguated ID Type / Status
Subject Middle Belt E12366 entity
Predicate includesState P285 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: [Middle Belt, includesState, Kwara State]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kwara State
Context triple: [Middle Belt, includesState, 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_69a4933208e88190891f5debab1b776d completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a496dc0aac8190afb75ec6c47a1d2d completed March 1, 2026, 7:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69a56930dfd88190a991adafc406c5ac completed March 2, 2026, 10:40 a.m.
Created at: March 1, 2026, 7:32 p.m.