Triple

T15219167
Position Surface form Disambiguated ID Type / Status
Subject Abucay E363715 entity
Predicate locatedNorthOf P305 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: [Abucay, locatedNorthOf, Balanga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Balanga
Context triple: [Abucay, locatedNorthOf, 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_69d85a0ce24c81909c4d3b6475548c95 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e007709d3881908384f0fe1e0218d0 completed April 15, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69fed345d58c81908a8fd182c0fe7c15 completed May 9, 2026, 6:25 a.m.
Created at: April 10, 2026, 3:11 a.m.