Triple

T12504821
Position Surface form Disambiguated ID Type / Status
Subject Nueva Vizcaya E298920 entity
Predicate borderedBy P224 FINISHED
Object Isabela E308468 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: Isabela | Statement: [Nueva Vizcaya, borderedBy, Isabela]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Isabela
Context triple: [Nueva Vizcaya, borderedBy, Isabela]
  • A. Isabela chosen
    Isabela is a large agricultural province in the Cagayan Valley region of the Philippines, known especially for its extensive rice and corn production.
  • B. Isabela
    Isabela is a coastal municipality in northwestern Puerto Rico known for its beaches, surfing spots, and scenic Atlantic shoreline.
  • C. Rosana
    Rosana is a municipality in the state of São Paulo, Brazil, known for hosting a campus of São Paulo State University (UNESP).
  • D. Cayetana
    Cayetana is the given name of Cayetana Fitz-James Stuart, the 18th Duchess of Alba, a prominent Spanish aristocrat known for holding a record number of noble titles.
  • E. Borbona
    Borbona is a small Italian town and comune in the Lazio region, known for its rural setting in the Apennine mountains and traditional local culture.
  • 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_69d6ada4cd388190ae3bbf83ff87057a completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94dfddf38819099263b8b1e804736 completed April 10, 2026, 7:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65eac74608190a6f1941ed5a05212 completed May 2, 2026, 8:29 p.m.
Created at: April 8, 2026, 9:57 p.m.