Triple

T11026616
Position Surface form Disambiguated ID Type / Status
Subject Beomjeon-dong E260640 entity
Predicate isSuburbOf P40 FINISHED
Object Busan E4279 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: Busan | Statement: [Beomjeon-dong, isSuburbOf, Busan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Busan
Context triple: [Beomjeon-dong, isSuburbOf, Busan]
  • A. Busan chosen
    Busan is South Korea’s second-largest city and a major international port known for its bustling harbor, beaches, and coastal scenery.
  • B. Changwon
    Changwon is a major industrial and administrative city in South Gyeongsang Province, South Korea, known for its planned urban layout and role as a regional government and manufacturing hub.
  • C. Ulsan
    Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
  • D. 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.
  • E. Daegu
    Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
  • 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_69d6aa9687448190b28d353b1b6a610e completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d797d0cd988190a7b21d7bdc3109ce completed April 9, 2026, 12:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69f78ac420788190b12167aef7436c64 completed May 3, 2026, 5:49 p.m.
Created at: April 8, 2026, 9:25 p.m.