Triple

T16978773
Position Surface form Disambiguated ID Type / Status
Subject Camiling E411885 entity
Predicate near P350 FINISHED
Object province of Pangasinan — NE NERFINISHED

How this triple was built (3 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: province of Pangasinan | Statement: [Camiling, near, province of Pangasinan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: province of Pangasinan
Context triple: [Camiling, near, province of Pangasinan]
  • A. Province of Pampanga
    The Province of Pampanga is a landlocked region in Central Luzon, Philippines, known as a major culinary center and economic hub with a strong history of trade, agriculture, and industry.
  • B. Province of Zambales
    The Province of Zambales is a coastal province in Central Luzon, Philippines, known for its beaches, coves, and the former U.S. naval base at Subic Bay.
  • C. Province of Bulacan
    The Province of Bulacan is a landlocked province in the Central Luzon region of the Philippines, known for its historical significance in the Philippine revolution, thriving industries, and rapidly urbanizing municipalities near Metro Manila.
  • D. Province of Isabela
    The Province of Isabela is a large agricultural province in the Cagayan Valley region of the Philippines, known for its vast rice and corn production and diverse natural landscapes.
  • E. Province of Samar
    The Province of Samar is a largely rural island province in the Eastern Visayas region of the Philippines, known for its rugged coastlines, dense forests, and role as a key landmass between the Samar Sea and Leyte Gulf.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: province of Pangasinan
Target entity description: The province of Pangasinan is a coastal province in the Ilocos Region of the Philippines, known for its salt-making industry, Hundred Islands National Park, and rich Ilocano and Pangasinense cultural heritage.
  • A. Province of Pampanga
    The Province of Pampanga is a landlocked region in Central Luzon, Philippines, known as a major culinary center and economic hub with a strong history of trade, agriculture, and industry.
  • B. Province of Zambales
    The Province of Zambales is a coastal province in Central Luzon, Philippines, known for its beaches, coves, and the former U.S. naval base at Subic Bay.
  • C. Province of Bulacan
    The Province of Bulacan is a landlocked province in the Central Luzon region of the Philippines, known for its historical significance in the Philippine revolution, thriving industries, and rapidly urbanizing municipalities near Metro Manila.
  • D. Province of Isabela
    The Province of Isabela is a large agricultural province in the Cagayan Valley region of the Philippines, known for its vast rice and corn production and diverse natural landscapes.
  • E. Province of Samar
    The Province of Samar is a largely rural island province in the Eastern Visayas region of the Philippines, known for its rugged coastlines, dense forests, and role as a key landmass between the Samar Sea and Leyte Gulf.
  • F. None of above. chosen

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_69d886ca8f348190812768ea8d5055ce completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d185a9408190a991bf8a1ef694f0 completed April 18, 2026, 6:46 p.m.
Created at: April 10, 2026, 5:32 a.m.