Triple

T5548871
Position Surface form Disambiguated ID Type / Status
Subject Narrow Bantu E145475 entity
Predicate includes P1393 FINISHED
Object Lingala language E54766 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: Lingala language | Statement: [Narrow Bantu, includes, Lingala language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lingala language
Context triple: [Narrow Bantu, includes, Lingala language]
  • A. Lingala chosen
    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.
  • B. Luba languages
    The Luba languages are a group of closely related Bantu languages spoken primarily in the Democratic Republic of the Congo by the Luba people and neighboring communities.
  • 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. Bemba language
    Bemba is a major Bantu language spoken primarily in Zambia, serving as one of the country’s most widely used lingua francas in daily life, education, and media.
  • 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_69c008fb879c81909f5bfa56fadc1d46 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c01fe143ec8190bb67d2530c92a419 completed March 22, 2026, 4:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0282dd7408190ad762fca9ff5e04b completed March 22, 2026, 5:34 p.m.
Created at: March 22, 2026, 3:35 p.m.