Triple

T8942357
Position Surface form Disambiguated ID Type / Status
Subject Kibuli Hill E212932 entity
Predicate region P40 FINISHED
Object Buganda E203300 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: Buganda | Statement: [Kibuli Hill, region, Buganda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Buganda
Context triple: [Kibuli Hill, region, Buganda]
  • A. Buganda chosen
    Buganda is a traditional Bantu kingdom in central Uganda that is the country’s most populous and historically influential region.
  • B. Nakasongola
    Nakasongola is a town in central Uganda that serves as an administrative and commercial center for the surrounding rural district.
  • C. Kanyaga
    "Kanyaga" is a popular Tanzanian Bongo Flava hit song by Diamond Platnumz known for its energetic beat and danceable style.
  • D. Bindura
    Bindura is a town in northern Zimbabwe that serves as the administrative and commercial center of the surrounding mining and agricultural region.
  • E. Bagamoyo
    Bagamoyo is a historic coastal town in present-day Tanzania that served as a major 19th-century East African trade and colonial center, including as an early administrative hub for German rule.
  • 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_69ca839694c88190b324ffeb43d23b08 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc66d93978819084b1a5c7c5dd2372 completed April 1, 2026, 12:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc1f418708190b1272209f61e3a51 completed April 3, 2026, 1:34 p.m.
Created at: March 30, 2026, 6:58 p.m.