Triple

T15859020
Position Surface form Disambiguated ID Type / Status
Subject 경기도 김포시 E384532 entity
Predicate hasRomanization P2508 FINISHED
Object Gimpo-si E226749 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: Gimpo-si | Statement: [경기도 김포시, hasRomanization, Gimpo-si]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gimpo-si
Context triple: [경기도 김포시, hasRomanization, Gimpo-si]
  • A. Gimpo chosen
    Gimpo is a city in northwestern South Korea known for its proximity to Seoul and its role as a transportation hub, including the location of Gimpo International Airport.
  • B. 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.
  • C. Sangil
    Sangil are an indigenous Moro ethnolinguistic group of the southern Philippines and nearby Indonesian islands, known for their seafaring traditions and Islamic faith.
  • D. Sinchon
    Sinchon is a vibrant university district in Seoul, South Korea, known for its dense concentration of colleges, youth culture, shopping, and nightlife.
  • E. Suwon
    Suwon is a major South Korean city best known for its UNESCO-listed Hwaseong Fortress and as a key cultural and economic center just south of 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_69d86da422088190aac39e32e6c68429 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1555956ec8190b13602a177e7a2bb completed April 16, 2026, 9:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffdbbff15c81909cb148a33b51a16e completed May 10, 2026, 1:13 a.m.
Created at: April 10, 2026, 4:50 a.m.