Triple

T16692236
Position Surface form Disambiguated ID Type / Status
Subject Ulsan plant E405622 entity
Predicate locatedIn P40 FINISHED
Object Ulsan Metropolitan City E28895 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: Ulsan Metropolitan City | Statement: [Ulsan plant, locatedIn, Ulsan Metropolitan City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ulsan Metropolitan City
Context triple: [Ulsan plant, locatedIn, Ulsan Metropolitan City]
  • A. Ulsan chosen
    Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
  • 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. 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. Gijeon
    Gijeon is an alternative name for the Seoul Capital Area, the densely populated metropolitan region surrounding South Korea’s capital city.
  • E. Daejeon
    Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
  • 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_69d8838c28748190b3f5967c743940ab completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e37eaacb948190954231c9e97a4adf completed April 18, 2026, 12:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a009d36aa90819090b738c1c94dcb9f completed May 10, 2026, 2:59 p.m.
Created at: April 10, 2026, 5:19 a.m.