Triple

T2118099
Position Surface form Disambiguated ID Type / Status
Subject Kirundi E43853 entity
Predicate closelyRelatedTo P37 FINISHED
Object Kinyarwanda E65621 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: Kinyarwanda | Statement: [Kirundi, closelyRelatedTo, Kinyarwanda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kinyarwanda
Context triple: [Kirundi, closelyRelatedTo, Kinyarwanda]
  • A. Kinyarwanda chosen
    Kinyarwanda is a Bantu language spoken primarily in Rwanda, where it serves as a national and widely used lingua franca.
  • B. Kirundi
    Kirundi is a Bantu language primarily spoken in Burundi and neighboring regions of East Africa.
  • C. Kikongo
    Kikongo is a Bantu language widely spoken in Central Africa, particularly in the western regions of the Democratic Republic of the Congo and neighboring countries.
  • D. Kimbundu
    Kimbundu is a major Bantu language spoken primarily in northwestern Angola, especially around the capital Luanda, by the Ambundu people.
  • E. Nyamwezi language
    The Nyamwezi language is a Bantu language spoken primarily by the Nyamwezi people of western-central Tanzania.
  • 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_69a88717cfe48190b7ecdd68c824848a completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abbb3117c081908c5e748a869d1f9f completed March 7, 2026, 5:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae307b08148190aa201ac038ce9944 completed March 9, 2026, 2:29 a.m.
Created at: March 4, 2026, 7:44 p.m.