Triple

T23473456
Position Surface form Disambiguated ID Type / Status
Subject Bayag E570191 entity
Predicate subdivisionOf P258 FINISHED
Object Isnag 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: Isnag language | Statement: [Bayag, subdivisionOf, Isnag language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Isnag language
Context triple: [Bayag, subdivisionOf, Isnag language]
  • A. Isnag language chosen
    The Isnag language is an Austronesian language spoken by the Isnag people in the northern Cordillera region of Luzon in the Philippines.
  • B. Nyishi language
    The Nyishi language is a Tani (Tibeto-Burman) language spoken primarily by the Nyishi people of Arunachal Pradesh in northeastern India.
  • C. Hunzib language
    The Hunzib language is a Northeast Caucasian (Nakh-Daghestanian) language spoken by a small community in Dagestan, Russia, known for its complex morphology and close relationship to other Tsezic languages.
  • D. Kimaragang language
    The Kimaragang language is an Austronesian language spoken by the Kimaragang people of Sabah, Malaysia, and is part of the Dusunic branch of the North Bornean languages.
  • E. Bafia language
    The Bafia language is a Bantu language spoken primarily by the Bafia people in central Cameroon.
  • 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_69e245af8a88819084f2704f6d265a92 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1a70244208190bbd8f58ac16d4399 completed April 29, 2026, 6:36 a.m.
Created at: April 17, 2026, 5:58 p.m.