Triple

T15200658
Position Surface form Disambiguated ID Type / Status
Subject National Route 1 (South Korea) E363258 entity
Predicate passesThrough P225 FINISHED
Object Incheon E27787 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: Incheon | Statement: [National Route 1 (South Korea), passesThrough, Incheon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Incheon
Context triple: [National Route 1 (South Korea), passesThrough, Incheon]
  • A. Incheon chosen
    Incheon is a major port city in northwestern South Korea, known for its international airport and role as a key transportation and economic hub.
  • B. Daegu
    Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
  • C. Daejeon
    Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
  • D. Ulsan
    Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
  • E. Busan
    Busan is South Korea’s second-largest city and a major international port known for its bustling harbor, beaches, and coastal scenery.
  • 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e006b588b88190a88e91d521acbdfe completed April 15, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff82e1b698819098930596327340d7 completed May 9, 2026, 6:54 p.m.
Created at: April 10, 2026, 3:10 a.m.