Triple

T15550922
Position Surface form Disambiguated ID Type / Status
Subject Federal University Dutsin-Ma E370740 entity
Predicate locatedIn P40 FINISHED
Object Dutsin-Ma E350192 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: Dutsin-Ma | Statement: [Federal University Dutsin-Ma, locatedIn, Dutsin-Ma]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dutsin-Ma
Context triple: [Federal University Dutsin-Ma, locatedIn, Dutsin-Ma]
  • A. Dutsin-Ma chosen
    Dutsin-Ma is a town in northern Nigeria known for hosting the Federal University Dutsin-Ma and serving as an important local commercial and educational center.
  • B. Tontemboan
    Tontemboan is an Austronesian language spoken by the Tontemboan people in North Sulawesi, Indonesia.
  • C. Abong-Mbang
    Abong-Mbang is a town in eastern Cameroon that serves as a local administrative and commercial center in the East Region.
  • D. Benina
    Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
  • E. Ngoumbi
    Ngoumbi is an alternative name for the Kombe people, an ethnic group of Central Africa.
  • 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_69d85cc6cf40819091f4a5facee1ebe6 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04a9551288190a583e8291c35f521 completed April 16, 2026, 2:33 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff455dfbcc8190a93e90c59b2d3045 completed May 9, 2026, 2:31 p.m.
Created at: April 10, 2026, 4:08 a.m.