Triple

T2013665
Position Surface form Disambiguated ID Type / Status
Subject Gyeonggi Province E43744 entity
Predicate hasCity P316 FINISHED
Object Pyeongtaek E42974 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: Pyeongtaek | Statement: [Gyeonggi Province, hasCity, Pyeongtaek]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pyeongtaek
Context triple: [Gyeonggi Province, hasCity, Pyeongtaek]
  • A. Pyeongtaek chosen
    Pyeongtaek is a South Korean city in Gyeonggi Province known for its major U.S. and UN military presence, including large bases such as Camp Humphreys.
  • B. Namyangju
    Namyangju is a city in South Korea known for its scenic natural landscapes, historical sites, and role as a suburban area within the Seoul metropolitan region.
  • C. Anseong
    Anseong is a city in Gyeonggi Province, South Korea, known for its traditional culture, agricultural heritage, and annual Baudeogi Festival.
  • D. Yongin
    Yongin is a rapidly growing city in the Seoul Capital Area of South Korea, known for attractions like Everland Resort and the Korean Folk Village.
  • E. Ansan
    Ansan is a coastal industrial city in South Korea known for its manufacturing base, multicultural population, and proximity to Seoul.
  • 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_69a88716e9f08190946313fdc949e3cf completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb8b42d508190bf2b63132bb2ad77 completed March 7, 2026, 5:33 a.m.
NED1 Entity disambiguation (via context triple) batch_69b589a2d510819089503b77f4d21fc8 completed March 14, 2026, 4:15 p.m.
Created at: March 4, 2026, 7:37 p.m.