Triple

T1307839
Position Surface form Disambiguated ID Type / Status
Subject Daegu E27919 entity
Predicate capitalOf P204 FINISHED
Object Daegu Metropolitan City 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: Daegu Metropolitan City | Statement: [Daegu, capitalOf, Daegu Metropolitan City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Daegu Metropolitan City
Context triple: [Daegu, capitalOf, Daegu Metropolitan City]
  • A. Daegu chosen
    Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
  • B. Daejeon
    Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
  • 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. Ulsan
    Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
  • E. Gwangju
    Gwangju is a major metropolitan city in southwestern South Korea known for its rich cultural heritage and pivotal role in the country’s pro-democracy movement.
  • 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_69a496d7d83481908f83085854e51328 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c13806b48190a0db33f8e5d53734 completed March 1, 2026, 10:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad36f6d9288190ad64dc1bc9e9f8c1 completed March 8, 2026, 8:44 a.m.
Created at: March 1, 2026, 7:51 p.m.