Triple

T9502326
Position Surface form Disambiguated ID Type / Status
Subject Saharan languages E229173 entity
Predicate majorLanguage P207 FINISHED
Object Daza language E750298 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: Daza language | Statement: [Saharan languages, majorLanguage, Daza language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Daza language
Context triple: [Saharan languages, majorLanguage, Daza language]
  • A. Dazaga language chosen
    The Dazaga language is a Saharan language spoken primarily by the Daza (Tebu) people in Chad, Niger, Libya, and surrounding regions.
  • B. Zaiwa language
    The Zaiwa language is a Tibeto-Burman language spoken primarily by the Zaiwa people in parts of Yunnan, China and northern Myanmar.
  • C. Dawan language
    The Dawan language is an Austronesian language spoken primarily in West Timor, Indonesia, by the Atoni people.
  • D. Daakaka language
    The Daakaka language is an Oceanic language spoken by communities on Ambrym Island in Vanuatu.
  • E. Zay language
    Zay language is a South Ethiopic Semitic language spoken by the Zay people on islands and shores of Lake Zway in Ethiopia.
  • 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_69d13a10f7b08190b8e4d4bc3b9815c6 completed April 4, 2026, 4:19 p.m.
Created at: March 30, 2026, 7:57 p.m.