Triple

T17061121
Position Surface form Disambiguated ID Type / Status
Subject Laur E413960 entity
Predicate hasLanguage P15 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: [Laur, hasLanguage, Kapampangan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kapampangan
Context triple: [Laur, hasLanguage, 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 (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_69d886cde3d481908d4d01ba88ba7eb7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3db7dea7481909e3e0bc836d27336 completed April 18, 2026, 7:29 p.m.
NED1 Entity disambiguation (via context triple) batch_6a01233cd3d48190b002951881ef670b completed May 11, 2026, 12:30 a.m.
Created at: April 10, 2026, 5:34 a.m.