Triple

T3199630
Position Surface form Disambiguated ID Type / Status
Subject Philippine languages E67018 entity
Predicate includesLanguage P2177 FINISHED
Object Kankanaey E304067 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: Kankanaey | Statement: [Philippine languages, includesLanguage, Kankanaey]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kankanaey
Context triple: [Philippine languages, includesLanguage, Kankanaey]
  • A. Kankanaey chosen
    Kankanaey are an indigenous ethnolinguistic group of the northern Philippines known for their terraced agriculture, rich oral traditions, and distinct language within the Cordillera region.
  • B. Canas Province
    Canas Province is an administrative division in southern Peru known for its high Andean landscapes, traditional Quechua-speaking communities, and cultural heritage within the Cusco Region.
  • C. Aklanon
    Aklanon is an Austronesian language spoken primarily in the province of Aklan in the central Philippines.
  • D. Bago Region
    Bago Region is an administrative division in central Myanmar known for its historical cities, agricultural economy, and role as a significant site of political unrest and protests.
  • E. Sarangani
    Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
  • 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_69ad8589bd988190afa7ed2bdffb7b33 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada9ad4b1c8190bc6ad0f025f238c8 completed March 8, 2026, 4:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b24bbdc9908190b5d8328f6fbc6002 completed March 12, 2026, 5:14 a.m.
Created at: March 8, 2026, 3:07 p.m.