Triple

T6120353
Position Surface form Disambiguated ID Type / Status
Subject Nyanja E136466 entity
Predicate alternativeName P39 FINISHED
Object Nyanja language E136466 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: Nyanja language | Statement: [Nyanja, alternativeName, Nyanja language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nyanja language
Context triple: [Nyanja, alternativeName, Nyanja language]
  • A. Nyamwezi language
    The Nyamwezi language is a Bantu language spoken primarily by the Nyamwezi people of western-central Tanzania.
  • B. Kinyankole language
    The Kinyankole language is a Bantu language spoken primarily by the Banyankole people in southwestern Uganda.
  • C. Ngindo language
    The Ngindo language is a Bantu language spoken by the Ngindo people of southeastern Tanzania.
  • D. Swahili language
    Swahili is a major Bantu language widely spoken in East and Central Africa, serving as a regional lingua franca and an official language in several countries including Tanzania and Kenya.
  • E. Nyanja chosen
    Nyanja is a major Bantu language spoken primarily in Malawi, Zambia, Mozambique, and Zimbabwe, known for serving as a lingua franca in parts of southern Africa.
  • 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_69c0089f851c81909e5e189a617dcff6 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c05bef8dc08190b917ad7209188c62 completed March 22, 2026, 9:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69c1257153748190947cd80589620f12 completed March 23, 2026, 11:35 a.m.
Created at: March 22, 2026, 4:14 p.m.