Triple

T23253664
Position Surface form Disambiguated ID Type / Status
Subject Mafa E581805 entity
Predicate language P15 FINISHED
Object Mafa language NE NERFINISHED

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: Mafa language | Statement: [Mafa, language, Mafa language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mafa language
Context triple: [Mafa, language, Mafa language]
  • A. Mafa language chosen
    The Mafa language is a Chadic language spoken by the Mafa people primarily in northern Cameroon and parts of northeastern Nigeria.
  • B. Mambae language
    The Mambae language is an Austronesian language spoken primarily in East Timor, notable for its role in local identity and traditional culture.
  • C. Mawase language
    The Mawase language is a Papuan language spoken in the Eastern Trans-Fly region of southern New Guinea.
  • D. Bafia language
    The Bafia language is a Bantu language spoken primarily by the Bafia people in central Cameroon.
  • E. Marau language
    The Marau language is an Oceanic language spoken in the Solomon Islands, belonging to the Southeast Solomonic branch of the Austronesian language family.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e24606b17c81908aba1a4911c8a8ba completed April 17, 2026, 2:39 p.m.
NER Named-entity recognition batch_69f193f9d904819097315c9bf031f667 completed April 29, 2026, 5:15 a.m.
Created at: April 17, 2026, 4:11 p.m.