Triple

T10001570
Position Surface form Disambiguated ID Type / Status
Subject Wenzhou E197338 entity
Predicate hasSisterCity P919 FINISHED
Object Cheonan E223590 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: Cheonan | Statement: [Wenzhou, hasSisterCity, Cheonan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cheonan
Context triple: [Wenzhou, hasSisterCity, Cheonan]
  • A. Cheonan chosen
    Cheonan is a major city in South Chungcheong Province, South Korea, known as a regional transportation hub and commercial center.
  • B. Pohang
    Pohang is a major industrial and port city in South Korea, best known as the home of the global steelmaker POSCO and a key hub on the country’s east coast.
  • C. 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.
  • D. Ulsan
    Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
  • E. Neryungri
    Neryungri is a major coal-mining and industrial city in southeastern Siberia, Russia, known as one of the key urban centers of the Sakha Republic (Yakutia).
  • 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_69ca82f3b61c81908ecc2c1c96dbc2e4 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdcc9078788190a4e75dd7ff830c63 completed April 2, 2026, 1:55 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b5f15c6c8190b52924d6f86d63e0 completed April 5, 2026, 7:20 p.m.
Created at: March 30, 2026, 8:51 p.m.