Triple

T6701044
Position Surface form Disambiguated ID Type / Status
Subject Proto-Bantu E152878 entity
Predicate ancestorOf P369 FINISHED
Object Kinyarwanda language 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 language | Statement: [Proto-Bantu, ancestorOf, Kinyarwanda language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kinyarwanda language
Context triple: [Proto-Bantu, ancestorOf, Kinyarwanda language]
  • 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. 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.
  • D. 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.
  • 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_69c68807adbc8190b8632df42b39eda0 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d0e4a1848190997520ddd7808cc6 completed March 27, 2026, 6:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69c70081395c8190921636db5cfa096a completed March 27, 2026, 10:11 p.m.
Created at: March 27, 2026, 2:05 p.m.