Triple

T7696012
Position Surface form Disambiguated ID Type / Status
Subject Rizal E174371 entity
Predicate borders P224 FINISHED
Object Bulacan E87267 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: Bulacan | Statement: [Rizal, borders, Bulacan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bulacan
Context triple: [Rizal, borders, Bulacan]
  • A. Bulacan chosen
    Bulacan is a province in the Central Luzon region of the Philippines known for its historical significance, cultural heritage, and proximity to Metro Manila.
  • B. 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.
  • C. Nueva Vizcaya
    Nueva Vizcaya was a vast province of New Spain in northern Mexico that once encompassed areas now forming several modern states, including Durango.
  • D. Nueva Vizcaya
    Nueva Vizcaya is a landlocked province in the northern Philippines known for its mountainous terrain, agricultural economy, and ethnically diverse population.
  • E. Quezon Province
    Quezon Province is a coastal province in the Calabarzon region of Luzon in the Philippines, known for its coconut plantations, heritage towns, and access to the Pacific Ocean.
  • 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_69c6995966348190939e6c37ba272c06 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c70267dab88190ac8e3f643343bf13 completed March 27, 2026, 10:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8acaa6004819088f1ae45ad9b378e completed March 29, 2026, 4:38 a.m.
Created at: March 27, 2026, 4:03 p.m.