Triple

T7261179
Position Surface form Disambiguated ID Type / Status
Subject Seo District E159654 entity
Predicate partOf P40 FINISHED
Object Incheon Metropolitan City E27787 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: Incheon Metropolitan City | Statement: [Seo District, partOf, Incheon Metropolitan City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Incheon Metropolitan City
Context triple: [Seo District, partOf, Incheon Metropolitan City]
  • A. Incheon chosen
    Incheon is a major port city in northwestern South Korea, known for its international airport and role as a key transportation and economic hub.
  • B. Busan metropolitan area
    The Busan metropolitan area is a major South Korean urban and economic hub centered on the port city of Busan, known for its extensive transportation links, coastal location, and role as a key gateway for international trade.
  • C. Sejong City
    Sejong City is South Korea’s planned administrative capital, designed to house numerous government ministries and ease congestion in Seoul.
  • D. Daejeon
    Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
  • E. 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.
  • 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_69c68838f9948190875fd60b2351230c completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6eac79fd081909274aa10ffb192aa completed March 27, 2026, 8:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69e15435faa881909b1a124f8027deeb completed April 16, 2026, 9:27 p.m.
Created at: March 27, 2026, 2:57 p.m.