Triple

T3199625
Position Surface form Disambiguated ID Type / Status
Subject Philippine languages E67018 entity
Predicate includesLanguage P2177 FINISHED
Object Masbateño E138031 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: Masbateño | Statement: [Philippine languages, includesLanguage, Masbateño]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Masbateño
Context triple: [Philippine languages, includesLanguage, Masbateño]
  • A. Masbateño chosen
    Masbateño is a Central Philippine Bisayan language spoken primarily on Masbate Island in the Philippines.
  • B. Masbate
    Masbate is an island province in the central Philippines, known for its cattle ranches, rodeo festivals, and location between Luzon and the Visayas.
  • C. Malpaso
    Malpaso is the highest peak on the Canary Island of El Hierro, known for its panoramic views over the island and surrounding Atlantic Ocean.
  • D. Mariquina
    Mariquina is a commune and town in southern Chile, located in the Los Ríos Region and known for its rural landscapes and Mapuche cultural presence.
  • E. Mazunte
    Mazunte is a small, laid-back beach town on Mexico’s Oaxacan coast, known for its sea turtle conservation center, eco-tourism, and scenic Pacific shoreline.
  • 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.