Triple

T3510066
Position Surface form Disambiguated ID Type / Status
Subject Osan, South Korea E74173 entity
Predicate isPartOf P10 FINISHED
Object Seoul Capital Area 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: Seoul Capital Area | Statement: [Osan, South Korea, isPartOf, Seoul Capital Area]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Seoul Capital Area
Context triple: [Osan, South Korea, isPartOf, Seoul Capital Area]
  • A. 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.
  • B. Seoul
    Seoul is the capital and largest metropolis of South Korea, known as a major global center for technology, culture, and finance.
  • C. Incheon
    Incheon is a major port city in northwestern South Korea, known for its international airport and role as a key transportation and economic hub.
  • D. Gyeonggi Province
    Gyeonggi Province is a populous region in northwestern South Korea that surrounds Seoul and serves as a key political, economic, and military hub of the country.
  • E. Daegu
    Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
  • 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbc0e1f0c8190b054d9fba16ce4b3 completed March 8, 2026, 6:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69b402d3a06481908e512c09306a2566 completed March 13, 2026, 12:28 p.m.
Created at: March 8, 2026, 3:18 p.m.