Triple

T4302765
Position Surface form Disambiguated ID Type / Status
Subject Morioka E99878 entity
Predicate sisterCity P1072 FINISHED
Object Pyeongchang, South Korea E413371 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: Pyeongchang, South Korea | Statement: [Morioka, sisterCity, Pyeongchang, South Korea]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pyeongchang, South Korea
Context triple: [Morioka, sisterCity, Pyeongchang, South Korea]
  • A. Pyeongchang chosen
    Pyeongchang is a county in South Korea best known internationally for hosting the 2018 Winter Olympics.
  • B. Osan, South Korea
    Osan is a city in Gyeonggi Province, South Korea, known for its proximity to Osan Air Base and its role as a transportation and commercial hub south of Seoul.
  • C. Suwon, South Korea
    Suwon, South Korea is a major city just south of Seoul known for its high-tech industry and the UNESCO-listed Hwaseong Fortress.
  • D. Jinju, South Korea
    Jinju, South Korea is a historic city in South Gyeongsang Province known for its riverside fortress, role in the Imjin War, and annual lantern festival.
  • E. Inchon, South Korea
    Inchon, South Korea is a major port city near Seoul known for its strategic coastal location and as the site of the pivotal Korean War amphibious landing.
  • 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_69b345528ebc8190b5abc7e95094792d completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b350b66450819089c9ff6ff9f045e5 completed March 12, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5c7507f1081909cf737dff00542d9 completed March 14, 2026, 8:38 p.m.
Created at: March 12, 2026, 11:08 p.m.