Triple

T11596246
Position Surface form Disambiguated ID Type / Status
Subject Runyankole E275007 entity
Predicate region P40 FINISHED
Object Ankole region E744386 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: Ankole region | Statement: [Runyankole, region, Ankole region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ankole region
Context triple: [Runyankole, region, Ankole region]
  • A. Nyanza region
    Nyanza region is an area in western Kenya along Lake Victoria, known for its predominantly Luo population and the city of Kisumu as its main urban center.
  • B. Vumba region
    The Vumba region is a scenic highland area in eastern Zimbabwe known for its lush forests, cool misty climate, and rich biodiversity, attracting nature lovers and tourists.
  • C. Mafinga region
    Mafinga Region is an administrative area in Tanzania that includes Mafinga Central and surrounding localities.
  • D. Ankole chosen
    Ankole was a traditional kingdom and region in southwestern Uganda, historically inhabited by the Banyankole people and known for its distinctive long-horned cattle.
  • E. Kagera Region
    Kagera Region is a northwestern region of Tanzania bordering Lake Victoria and several East African countries, known for its diverse ethnic groups, agriculture, and historical significance.
  • 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_69d6aae6b14c81908dc5a74bad7591f9 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8946790d08190924d60bb4b523250 completed April 10, 2026, 6:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69ef8269c4f48190aaf238a64c5caf1d completed April 27, 2026, 3:36 p.m.
Created at: April 8, 2026, 9:38 p.m.