Triple

T17466702
Position Surface form Disambiguated ID Type / Status
Subject Angono E425294 entity
Predicate locatedNear P294 FINISHED
Object Binangonan 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: Binangonan | Statement: [Angono, locatedNear, Binangonan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Binangonan
Context triple: [Angono, locatedNear, Binangonan]
  • A. Binangonan chosen
    Binangonan is a lakeside municipality in the province of Rizal, Philippines, known for its fishing communities, scenic views, and proximity to Metro Manila.
  • B. Bungsuan
    Bungsuan is a barangay (village-level administrative division) of the municipality of Dumalag in the province of Capiz, Philippines.
  • C. Binantayanon
    Binantayanon is an Austronesian language variety spoken on Bantayan Island in the Philippines, closely related to Cebuano and other Visayan languages.
  • D. Babatngon
    Babatngon is a coastal municipality in the province of Leyte in the Philippines, known for its fishing industry and rural communities.
  • E. Binuangan
    Binuangan is a coastal municipality in the province of Misamis Oriental in the Philippines, known for its fishing communities and rural character.
  • 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_69d889dbc2e88190b18ea6115e819258 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e451a7e2a481908c32defa3401f848 completed April 19, 2026, 3:53 a.m.
Created at: April 10, 2026, 5:47 a.m.