Triple

T5745270
Position Surface form Disambiguated ID Type / Status
Subject Subanen E126716 entity
Predicate language P15 FINISHED
Object Subanen language E150507 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: Subanen language | Statement: [Subanen, language, Subanen language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Subanen language
Context triple: [Subanen, language, Subanen language]
  • A. Subanen languages chosen
    The Subanen languages are a group of closely related Austronesian languages spoken by the Subanen people in the Zamboanga Peninsula and nearby areas of Mindanao in the southern Philippines.
  • B. Tubatulabal language
    The Tubatulabal language is an endangered Uto-Aztecan language traditionally spoken by the Tubatulabal people of the southern Sierra Nevada region in California.
  • C. Bambam language
    The Bambam language is an Austronesian language spoken in parts of South Sulawesi, Indonesia, known for its place within the region’s diverse indigenous linguistic landscape.
  • D. Mampruli language
    Mampruli is a Gur language spoken primarily by the Mamprusi people in northern Ghana and parts of neighboring West African countries.
  • E. Baniwa language
    Baniwa is an Arawakan Indigenous language spoken primarily along the Rio Negro in northwestern Brazil, as well as in parts of Colombia and Venezuela.
  • 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_69c0083179548190b384b0bf3c08ca4d completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c025883b608190b21523da2afde218 completed March 22, 2026, 5:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07e27ba848190b0c289a804b865cd completed March 22, 2026, 11:41 p.m.
Created at: March 22, 2026, 3:48 p.m.