Triple

T8585693
Position Surface form Disambiguated ID Type / Status
Subject Buganda E203300 entity
Predicate language P15 FINISHED
Object Luganda E56177 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: Luganda | Statement: [Buganda, language, Luganda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Luganda
Context triple: [Buganda, language, Luganda]
  • A. Luganda chosen
    Luganda is a major Bantu language spoken primarily in Uganda, serving as a key lingua franca and cultural language of the Baganda people.
  • B. Kitwe
    Kitwe is a major mining and industrial city in Zambia’s Copperbelt Province, known as one of the country’s largest urban and economic centers.
  • C. Tumbuka
    Tumbuka is a Bantu language spoken primarily in northern Malawi and parts of Zambia and Tanzania.
  • D. Kinyankole language
    The Kinyankole language is a Bantu language spoken primarily by the Banyankole people in southwestern Uganda.
  • E. Sukuma–Nyamwezi languages
    The Sukuma–Nyamwezi languages are a closely related group of Bantu languages spoken primarily in northwestern Tanzania by the Sukuma, Nyamwezi, and neighboring ethnic groups.
  • 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_69ca8329bb7c8190a63c643730839103 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cc457ab8b08190a53c730417288deb completed March 31, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69cea89e4658819090cc6e94e934670b completed April 2, 2026, 5:34 p.m.
Created at: March 30, 2026, 6:22 p.m.