Triple

T9502315
Position Surface form Disambiguated ID Type / Status
Subject Saharan languages E229173 entity
Predicate hasMemberLanguage P7390 FINISHED
Object Kanembu language E749762 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: Kanembu language | Statement: [Saharan languages, hasMemberLanguage, Kanembu language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kanembu language
Context triple: [Saharan languages, hasMemberLanguage, Kanembu language]
  • A. Kanembu language chosen
    The Kanembu language is a Saharan language spoken primarily in Chad by the Kanembu people, closely related to the Tebu languages and used historically in the Kanem-Bornu Empire.
  • B. Kumbewaha language
    The Kumbewaha language is an Austronesian language spoken in Sulawesi, Indonesia, belonging to the Wotu–Wolio subgroup.
  • C. Kawaiisu language
    Kawaiisu language is an endangered Uto-Aztecan language traditionally spoken by the Kawaiisu people of southern California.
  • D. Nambya language
    Nambya is a Bantu language spoken primarily in northwestern Zimbabwe and northeastern Botswana, closely related to Kalanga and used by the Nambya people.
  • E. Akawaio language
    The Akawaio language is an indigenous Cariban language spoken by the Akawaio people of Guyana, Venezuela, and Brazil.
  • 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_69ca847611c48190a28c028644198c75 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd983ea6048190a2d7924c8e6d1fbc completed April 1, 2026, 10:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14c02de448190a2feea16d5461726 completed April 4, 2026, 5:36 p.m.
Created at: March 30, 2026, 7:57 p.m.