Triple

T6686500
Position Surface form Disambiguated ID Type / Status
Subject North Gyeongsang Province E152110 entity
Predicate formerCapital P3417 FINISHED
Object Daegu 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 | Statement: [North Gyeongsang Province, formerCapital, Daegu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Daegu
Context triple: [North Gyeongsang Province, formerCapital, Daegu]
  • 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. Ulsan
    Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
  • D. 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.
  • E. 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.
  • 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_69c687f9977c819097e7f5ada4fe522e completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6b14cd6748190aad4badd5f253478 completed March 27, 2026, 4:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69c988df1e4c8190a46c971a3f9b2d49 completed March 29, 2026, 8:17 p.m.
Created at: March 27, 2026, 2:04 p.m.