Triple

T12051857
Position Surface form Disambiguated ID Type / Status
Subject Northern Region, Uganda E286934 entity
Predicate hasLanguage 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: [Northern Region, Uganda, hasLanguage, Luganda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Luganda
Context triple: [Northern Region, Uganda, hasLanguage, 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_69d6ab4780948190bdb9f7620c2ac27e completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d90423b22081908fba82fbc6b40eb5 completed April 10, 2026, 2:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f64f40388190bfb3d2a81d5fbf5e completed May 2, 2026, 1:04 p.m.
Created at: April 8, 2026, 9:47 p.m.