Triple

T3199616
Position Surface form Disambiguated ID Type / Status
Subject Philippine languages E67018 entity
Predicate includesLanguage P2177 FINISHED
Object Kapampangan E82018 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: Kapampangan | Statement: [Philippine languages, includesLanguage, Kapampangan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kapampangan
Context triple: [Philippine languages, includesLanguage, Kapampangan]
  • A. Kapampangan chosen
    Kapampangan is an Austronesian language spoken primarily in the Pampanga region of the Philippines by the Kapampangan ethnic group.
  • B. Ibanag
    Ibanag is an Austronesian language spoken primarily in the Cagayan Valley region of northern Luzon in the Philippines.
  • C. Boholano
    Boholano is a regional variety of the Cebuano (Binisaya) language spoken primarily on the island of Bohol in the Philippines.
  • D. Yakan
    Yakan is an Austronesian language spoken primarily by the Yakan people of Basilan and nearby areas in the southern Philippines.
  • E. Nueva Ecija
    Nueva Ecija is a landlocked agricultural province in Central Luzon, Philippines, known as a major rice-producing area and home to diverse ethnolinguistic groups.
  • 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.