Triple

T17147044
Position Surface form Disambiguated ID Type / Status
Subject Naga, Cebu E416116 entity
Predicate borderedBy P224 FINISHED
Object Talisay, Cebu E364591 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: Talisay, Cebu | Statement: [Naga, Cebu, borderedBy, Talisay, Cebu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Talisay, Cebu
Context triple: [Naga, Cebu, borderedBy, Talisay, Cebu]
  • A. Talisay City chosen
    Talisay City is a coastal component city in the province of Cebu in the Philippines, known for its historical significance and proximity to Metro Cebu.
  • B. Talisay, Batangas
    Talisay, Batangas is a lakeside municipality in the Philippines known as a primary gateway to Taal Volcano and its scenic crater lake.
  • C. Talisay
    Talisay is a coastal municipality in the Philippine province of Camarines Norte known for its rural communities and access to fishing and agricultural resources.
  • D. Talisay
    Talisay is a coastal barangay of the municipality of Daanbantayan in northern Cebu, Philippines.
  • E. Talisay
    Talisay is a city in the Philippine province of Negros Occidental known for its sugarcane industry and historical landmarks.
  • 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_69d886d15af4819092f92f8a129763e6 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3f2db20d48190b5d69ccf89f3bc42 completed April 18, 2026, 9:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a01483158348190abb96b36caaf455a completed May 11, 2026, 3:08 a.m.
Created at: April 10, 2026, 5:36 a.m.