Triple

T20676388
Position Surface form Disambiguated ID Type / Status
Subject Ministry of Oceans and Fisheries of South Korea E508169 entity
Predicate headquartersLocation P62 FINISHED
Object Sejong City NE NERFINISHED

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: Sejong City | Statement: [Ministry of Oceans and Fisheries of South Korea, headquartersLocation, Sejong City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sejong City
Context triple: [Ministry of Oceans and Fisheries of South Korea, headquartersLocation, Sejong City]
  • A. Sejong City chosen
    Sejong City is South Korea’s planned administrative capital, designed to house numerous government ministries and ease congestion in Seoul.
  • 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. Daejeon
    Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
  • 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. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4c1164881909a3bf1e3ddb2bc32 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6b5cf18b48190be6995e197946517 completed April 20, 2026, 11:25 p.m.
Created at: April 16, 2026, 11:44 a.m.