Triple

T12832103
Position Surface form Disambiguated ID Type / Status
Subject Pangasinan people E306813 entity
Predicate nativeName P15 FINISHED
Object Pangasinense E526944 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: Pangasinense | Statement: [Pangasinan people, nativeName, Pangasinense]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pangasinense
Context triple: [Pangasinan people, nativeName, Pangasinense]
  • A. Pangasinense chosen
    Pangasinense is an Austronesian language spoken primarily in the province of Pangasinan in the Philippines.
  • B. Nueva Ecija
    Nueva Ecija is a landlocked agricultural province in Central Luzon, Philippines, known as a major rice-producing area and home to diverse ethnolinguistic groups.
  • C. Sorsogon
    Sorsogon is a province in the Bicol Region of the Philippines known for its coastal landscapes, whale shark interactions in Donsol, and rich Bikolano culture.
  • D. 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.
  • E. Tarlac
    Tarlac is a landlocked province in the Central Luzon region of the Philippines known for its culturally diverse population and agricultural economy.
  • 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_69d7bdf52b94819096d6f0ba4ab50a98 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96fb0bb208190bdc4d3dc7909be06 completed April 10, 2026, 9:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a007581ba008190a6d558c8f4e861d6 completed May 10, 2026, 12:09 p.m.
Created at: April 9, 2026, 5:34 p.m.