Triple

T15763800
Position Surface form Disambiguated ID Type / Status
Subject Nasarawa State University, Keffi E382166 entity
Predicate city P40 FINISHED
Object Keffi E368492 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: Keffi | Statement: [Nasarawa State University, Keffi, city, Keffi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Keffi
Context triple: [Nasarawa State University, Keffi, city, Keffi]
  • A. Keffi chosen
    Keffi is a historic town and commercial center in central Nigeria that serves as one of the key urban settlements in Nasarawa State.
  • B. Abakaliki
    Abakaliki is a major city in southeastern Nigeria known as an administrative, commercial, and agricultural hub.
  • C. Jalingo
    Jalingo is the capital and largest city of Taraba State in northeastern Nigeria, serving as an important administrative and commercial center for the region.
  • D. Mbuji-Mayi
    Mbuji-Mayi is a major city in south-central Democratic Republic of the Congo, known as a key center of the country’s diamond mining industry.
  • E. Makurdi
    Makurdi is the capital city of Benue State in central Nigeria, serving as an important administrative and commercial hub in the 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_69d86da09a10819082fe9797b23e4664 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e050b6c9fc8190a1bcf763c4b04b12 completed April 16, 2026, 3 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff8776c2488190ad27fd79e2ce4e14 completed May 9, 2026, 7:13 p.m.
Created at: April 10, 2026, 4:47 a.m.