Triple

T7213654
Position Surface form Disambiguated ID Type / Status
Subject Ibanag language E149471 entity
Predicate hasDialect P4251 FINISHED
Object North Ibanag E101548 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: North Ibanag | Statement: [Ibanag language, hasDialect, North Ibanag]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: North Ibanag
Context triple: [Ibanag language, hasDialect, North Ibanag]
  • A. Aguiguan
    Aguiguan is a small, uninhabited island in the Northern Mariana Islands known for its rugged terrain and seabird colonies.
  • B. Ibanag chosen
    Ibanag is an Austronesian language spoken primarily in the Cagayan Valley region of northern Luzon in the Philippines.
  • C. Balanga
    Balanga is a coastal city in the province of Bataan in the Philippines, situated along the shores of Manila Bay.
  • D. Bukidnon
    Bukidnon is a landlocked, mountainous province in the Philippines known for its vast agricultural plantations, cool climate, and scenic highland landscapes.
  • E. Guinsiliban
    Guinsiliban is a coastal municipality on the island-province of Camiguin in the Philippines, known for its rural communities and proximity to volcanic landscapes and marine attractions.
  • 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_69c687eca814819095abb52316b1af80 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6e98cbebc8190941e76259c988790 completed March 27, 2026, 8:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7bfd41a8881909dc94c01f6601b2e completed March 28, 2026, 11:47 a.m.
Created at: March 27, 2026, 2:53 p.m.