Triple

T2282306
Position Surface form Disambiguated ID Type / Status
Subject Kituba E51306 entity
Predicate closelyRelatedTo P37 FINISHED
Object Kikongo E57354 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: Kikongo | Statement: [Kituba, closelyRelatedTo, Kikongo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kikongo
Context triple: [Kituba, closelyRelatedTo, Kikongo]
  • A. Kikongo chosen
    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.
  • B. Lingala
    Lingala is a Bantu language widely spoken as a lingua franca in the Democratic Republic of the Congo and the Republic of the Congo, especially in urban centers and along the Congo River.
  • C. Kimbundu
    Kimbundu is a major Bantu language spoken primarily in northwestern Angola, especially around the capital Luanda, by the Ambundu people.
  • D. Kirundi
    Kirundi is a Bantu language primarily spoken in Burundi and neighboring regions of East Africa.
  • E. Kituba
    Kituba is a widely spoken Bantu-based creole language of Central Africa, serving as a major lingua franca in the Republic of the Congo and surrounding regions.
  • 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_69a88b08e4308190bdac9aebcca1c91a completed March 4, 2026, 7:42 p.m.
NER Named-entity recognition batch_69abc21d6d748190980128c1bc5b9621 completed March 7, 2026, 6:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae96080548819098ae6c5ab73036f2 completed March 9, 2026, 9:42 a.m.
Created at: March 4, 2026, 7:48 p.m.