Triple

T16236552
Position Surface form Disambiguated ID Type / Status
Subject Western Luzon E394126 entity
Predicate hasMajorCity P316 FINISHED
Object Balanga E254766 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: Balanga | Statement: [Western Luzon, hasMajorCity, Balanga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Balanga
Context triple: [Western Luzon, hasMajorCity, Balanga]
  • A. Balanga chosen
    Balanga is a coastal city in the province of Bataan in the Philippines, situated along the shores of Manila Bay.
  • B. Balanga
    Balanga is a local government area in Gombe State, northeastern Nigeria, known for its agrarian communities and the Balanga Dam.
  • C. Kapangan
    Kapangan is a rural municipality in the mountainous province of Benguet in the Philippines, known for its cool climate, highland farms, and scenic Cordillera landscapes.
  • D. Lubuagan
    Lubuagan is a landlocked, mountainous municipality in the Philippine province of Kalinga known for its rich indigenous culture and history.
  • E. Bucoda
    Bucoda is a small town in Thurston County, Washington, known for its historic coal-mining roots and its claim as the "World's Tiniest Town with the Biggest Halloween Spirit."
  • 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_69d87f204df88190a8f88923decf9835 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2455abc608190ba3308c15c9e8a23 completed April 17, 2026, 2:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a000ed8cbe48190be68ccade55211ad completed May 10, 2026, 4:51 a.m.
Created at: April 10, 2026, 5:04 a.m.