Triple

T13088003
Position Surface form Disambiguated ID Type / Status
Subject Bataan E310386 entity
Predicate hasMunicipality P847 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: [Bataan, hasMunicipality, Balanga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Balanga
Context triple: [Bataan, hasMunicipality, 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. Lubuagan
    Lubuagan is a landlocked, mountainous municipality in the Philippine province of Kalinga known for its rich indigenous culture and history.
  • C. 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."
  • D. Bayambang
    Bayambang is a municipality in the province of Pangasinan in the Philippines, known for its agricultural economy and historical significance dating back to the Spanish colonial period.
  • E. Cauayan
    Cauayan is a rapidly developing component city located in the province of Isabela in the Cagayan Valley region of the Philippines.
  • 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_69d806a733548190989cfd4ce981ca33 completed April 9, 2026, 8:05 p.m.
NER Named-entity recognition batch_69d981378dd08190b4f00e4e5df0e480 completed April 10, 2026, 11:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7265eba0481908417d2bb905874e5 completed May 3, 2026, 10:41 a.m.
Created at: April 9, 2026, 9:02 p.m.