Triple

T14701536
Position Surface form Disambiguated ID Type / Status
Subject Potiskum E345311 entity
Predicate hasNearbyCity P350 FINISHED
Object Gombe E61525 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: Gombe | Statement: [Potiskum, hasNearbyCity, Gombe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gombe
Context triple: [Potiskum, hasNearbyCity, Gombe]
  • A. Gombe
    Gombe is a region in western Tanzania best known for its national park where pioneering primatologist Jane Goodall conducted her landmark chimpanzee research.
  • B. Gombe chosen
    Gombe is a major city in northeastern Nigeria that serves as the capital and economic hub of Gombe State.
  • C. Mbewuleni
    Mbewuleni is a rural village in South Africa’s Eastern Cape province, best known as the birthplace of former South African president Thabo Mbeki.
  • D. Tondibi
    Tondibi is a historic site in present-day Mali known as the location of a decisive 1591 battle that led to the fall of the Songhai Empire.
  • E. Nyala
    Nyala is a major city in western Sudan that serves as a key commercial and administrative center in the Darfur 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_69d822e4a8c08190a155df736bb7bc13 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb605f5948190ab6b20887c4b6833 completed April 14, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdf0841da48190991d5045954a32ab completed May 8, 2026, 2:17 p.m.
Created at: April 10, 2026, 1:28 a.m.