Triple

T8525614
Position Surface form Disambiguated ID Type / Status
Subject 북구 E201807 entity
Predicate usedAsDistrictNameIn P50586 FINISHED
Object 대구광역시 E27919 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: 대구광역시 | Statement: [북구, usedAsDistrictNameIn, 대구광역시]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: 대구광역시
Context triple: [북구, usedAsDistrictNameIn, 대구광역시]
  • A. Daegu chosen
    Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
  • 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. Daejeon
    Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
  • D. Dongducheon
    Dongducheon is a city in northern South Korea known for its proximity to the Demilitarized Zone and the presence of U.S. military bases.
  • E. 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.
  • 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_69ca83228b24819085d22e7dc99f5d94 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cc4578d9c8819096b3853d01c3ec11 completed March 31, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce6d3fabb08190a3ad63f9153ad44c completed April 2, 2026, 1:21 p.m.
Created at: March 30, 2026, 6:16 p.m.