Triple

T12468060
Position Surface form Disambiguated ID Type / Status
Subject SBS E297976 entity
Predicate broadcastArea P2441 FINISHED
Object metropolitan Seoul E166134 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: metropolitan Seoul | Statement: [SBS, broadcastArea, metropolitan Seoul]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: metropolitan Seoul
Context triple: [SBS, broadcastArea, metropolitan Seoul]
  • A. central Seoul
    Central Seoul is the bustling core area of South Korea’s capital city, encompassing major government, commercial, and cultural districts.
  • B. Seoul Capital Area chosen
    The Seoul Capital Area is South Korea’s largest metropolitan region, encompassing Seoul, Incheon, and surrounding Gyeonggi Province, and serving as the country’s political, economic, and cultural hub.
  • C. 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.
  • D. Ilsan New Town
    Ilsan New Town is a planned residential and commercial district in Goyang, South Korea, known for its modern urban design, parks, and convenient access to Seoul.
  • E. Seoul
    Seoul is the capital and largest metropolis of South Korea, known as a major global center for technology, culture, and finance.
  • 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_69d6ada270808190b1a2b2e7b02bb426 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94db979c481908778188794b2c08e completed April 10, 2026, 7:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6556aae84819083e4988ec6f21471 completed May 2, 2026, 7:50 p.m.
Created at: April 8, 2026, 9:56 p.m.