Triple

T3510052
Position Surface form Disambiguated ID Type / Status
Subject Osan, South Korea E74173 entity
Predicate hasRailwayLine P848 FINISHED
Object Seoul Subway Line 1 E105452 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: Seoul Subway Line 1 | Statement: [Osan, South Korea, hasRailwayLine, Seoul Subway Line 1]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Seoul Subway Line 1
Context triple: [Osan, South Korea, hasRailwayLine, Seoul Subway Line 1]
  • A. Seoul Metropolitan Subway chosen
    The Seoul Metropolitan Subway is an extensive rapid transit network serving Seoul and its surrounding metropolitan area, known for its high efficiency, cleanliness, and technological sophistication.
  • B. Incheon Subway
    Incheon Subway is the urban rapid transit system serving the city of Incheon, South Korea, connecting major districts and linking with the Seoul Metropolitan Subway network.
  • C. Daegu Metro
    Daegu Metro is the urban rapid transit system serving the city of Daegu in South Korea, providing high-capacity rail transportation across the metropolitan area.
  • D. Gwangju Metro
    Gwangju Metro is the urban rapid transit system serving the city of Gwangju in South Korea.
  • E. Daejeon Metro
    Daejeon Metro is the urban rapid transit system serving the city of Daejeon in South Korea.
  • 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbc0e1f0c8190b054d9fba16ce4b3 completed March 8, 2026, 6:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69b373e71ae8819096ea39955c92076a completed March 13, 2026, 2:18 a.m.
Created at: March 8, 2026, 3:18 p.m.