Triple

T17082443
Position Surface form Disambiguated ID Type / Status
Subject Rizal, Nueva Ecija E414506 entity
Predicate countrySubdivision P766 FINISHED
Object Nueva Ecija 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: Nueva Ecija | Statement: [Rizal, Nueva Ecija, countrySubdivision, Nueva Ecija]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nueva Ecija
Context triple: [Rizal, Nueva Ecija, countrySubdivision, Nueva Ecija]
  • A. Nueva Ecija chosen
    Nueva Ecija is a landlocked agricultural province in Central Luzon, Philippines, known as a major rice-producing area and home to diverse ethnolinguistic groups.
  • B. Tarlac
    Tarlac is a landlocked province in the Central Luzon region of the Philippines known for its culturally diverse population and agricultural economy.
  • C. Pampanga
    Pampanga is a province in the Central Luzon region of the Philippines, known for its rich culinary heritage, vibrant festivals, and significant role in the country’s history and culture.
  • D. Pangasinense
    Pangasinense is an Austronesian language spoken primarily in the province of Pangasinan in the Philippines.
  • E. Pangasinan
    Pangasinan is an Austronesian language spoken primarily in the Pangasinan province and surrounding areas of northwestern Luzon in the Philippines.
  • 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_69d886cef44c8190ba56c44b4e863e64 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dbe408d48190b4f52c2102eae7c2 completed April 18, 2026, 7:30 p.m.
Created at: April 10, 2026, 5:35 a.m.