Triple

T9413086
Position Surface form Disambiguated ID Type / Status
Subject Uiwang E226750 entity
Predicate hasNeighboringCity P3883 FINISHED
Object Gwacheon E442655 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: Gwacheon | Statement: [Uiwang, hasNeighboringCity, Gwacheon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gwacheon
Context triple: [Uiwang, hasNeighboringCity, Gwacheon]
  • A. Gwacheon chosen
    Gwacheon is a small city in South Korea known for hosting major government offices, cultural institutions, and the Seoul Grand Park complex.
  • B. 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.
  • C. Ansan
    Ansan is a coastal industrial city in South Korea known for its manufacturing base, multicultural population, and proximity to Seoul.
  • D. Dongducheon
    Dongducheon is a city in northern South Korea known for its proximity to the Demilitarized Zone and the presence of U.S. military bases.
  • E. Uijeongbu
    Uijeongbu is a city in South Korea known as a suburban hub north of Seoul, featuring residential districts, commercial centers, and a history of hosting U.S. military bases.
  • 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_69ca843280488190bc65600e843ef9e6 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd5258f7e081908d48600409181fdb completed April 1, 2026, 5:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69d107b0f9648190894a4cd13d7e5fb5 completed April 4, 2026, 12:44 p.m.
Created at: March 30, 2026, 7:47 p.m.