Triple

T7994205
Position Surface form Disambiguated ID Type / Status
Subject Singida Region E186081 entity
Predicate languageUsed P238 FINISHED
Object Sukuma language E181669 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: Sukuma language | Statement: [Singida Region, languageUsed, Sukuma language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sukuma language
Context triple: [Singida Region, languageUsed, Sukuma language]
  • A. Sukuma language chosen
    The Sukuma language is a Bantu language spoken primarily by the Sukuma people in northwestern Tanzania.
  • B. Kisukuma language
    Kisukuma is a major Bantu language spoken primarily by the Sukuma people in northwestern Tanzania.
  • C. Suma language
    The Suma language is a lesser-known Gbaya language spoken by the Suma people in parts of Central Africa.
  • D. Kamba language
    Kamba language is a Bantu language spoken primarily by the Kamba people of Kenya, known for its rich oral traditions and close linguistic ties to other Central Kenya Bantu languages.
  • E. Misima-Paneati language
    The Misima-Paneati language is an Oceanic language spoken in the Milne Bay Province of Papua New Guinea, primarily on Misima and nearby islands.
  • 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_69ca829c6c308190ab05b43d234c52b2 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3c73ba388190bcedc29fbdd22f3c completed March 31, 2026, 3:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69cbe105d400819096ba271416bb24e7 completed March 31, 2026, 2:58 p.m.
Created at: March 30, 2026, 5:16 p.m.