Triple

T20840727
Position Surface form Disambiguated ID Type / Status
Subject Sto. Rosario E513090 entity
Predicate languageUsed P238 FINISHED
Object Kapampangan 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: Kapampangan | Statement: [Sto. Rosario, languageUsed, Kapampangan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kapampangan
Context triple: [Sto. Rosario, languageUsed, 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. Sugbuanon
    Sugbuanon refers to the Cebuano people, a Visayan ethnolinguistic group from the central and southern Philippines known for speaking the Cebuano language.
  • D. Surigaonon Bisaya
    Surigaonon Bisaya is a Visayan language variety spoken primarily in Surigao and nearby areas in the northeastern part of Mindanao in the Philippines.
  • E. Sugbu
    Sugbu is the pre-colonial name for the area that became Cebu City in the Philippines, a major coastal settlement and trading center in the Visayas.
  • 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_69e0b4cf62a88190bbf92351e9e57259 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c34b17b88190b3290bd5100ad2ad completed April 21, 2026, 12:22 a.m.
Created at: April 16, 2026, 12:43 p.m.