Triple

T2054340
Position Surface form Disambiguated ID Type / Status
Subject Binisaya E45639 entity
Predicate closelyRelatedTo P37 FINISHED
Object Waray E10736 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: Waray | Statement: [Binisaya, closelyRelatedTo, Waray]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Waray
Context triple: [Binisaya, closelyRelatedTo, Waray]
  • A. Waray language chosen
    Waray is an Austronesian language spoken primarily in the Eastern Visayas region of the Philippines, particularly on Samar and nearby islands.
  • B. Binisaya
    Binisaya is a major Austronesian language of the Philippines, widely spoken in the Central Visayas and parts of Mindanao.
  • C. Butuanon language
    The Butuanon language is an Austronesian language spoken primarily in and around Butuan City in Mindanao, Philippines.
  • D. Kapampangan language
    Kapampangan is an Austronesian language of the Philippines primarily spoken in the Pampanga region of Central Luzon.
  • E. Bikol language
    The Bikol language is an Austronesian language spoken primarily in the Bicol Region of the Philippines, known for its several regional varieties and close relation to other Central Philippine languages.
  • 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_69a8891a19508190a12ef1e192308dcb completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb9a8518081909ba95a8ef9321f12 completed March 7, 2026, 5:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae200eb09881908bbfe47ebb62f55e completed March 9, 2026, 1:19 a.m.
Created at: March 4, 2026, 7:39 p.m.