Triple

T14068070
Position Surface form Disambiguated ID Type / Status
Subject Kigezi Highlands E338527 entity
Predicate contains P35 FINISHED
Object Kabale District E1044328 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: Kabale District | Statement: [Kigezi Highlands, contains, Kabale District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kabale District
Context triple: [Kigezi Highlands, contains, Kabale District]
  • A. Kabale District chosen
    Kabale District is an administrative district in southwestern Uganda known for its hilly terrain, cool climate, and proximity to Lake Bunyonyi.
  • B. Pallisa District
    Pallisa District is an administrative district in eastern Uganda known for its predominantly rural communities and agriculture-based economy.
  • C. Kotido District
    Kotido District is an administrative district in northeastern Uganda known for its predominantly pastoralist Karamojong communities and semi-arid landscape.
  • D. Kalomo District
    Kalomo District is an administrative district in southern Zambia known for its agricultural activities and proximity to national parks such as Kafue.
  • E. Rezina District
    Rezina District is an administrative region in eastern Moldova known for its location along the Răut River and its mix of rural communities and historical sites.
  • 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_69d81c67ba6c819091935650dfb3b895 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de568b81f08190a571004261c0e8e4 completed April 14, 2026, 3 p.m.
NED1 Entity disambiguation (via context triple) batch_69fde15cbbb0819099b84032d65cfdb0 completed May 8, 2026, 1:13 p.m.
Created at: April 9, 2026, 10:21 p.m.