Triple

T19734409
Position Surface form Disambiguated ID Type / Status
Subject Gyeongui–Jungang Line E473938 entity
Predicate hasStation P35 FINISHED
Object Haengsin Station
Haengsin Station is a railway station in South Korea that serves as a stop on Seoul’s Gyeongui–Jungang commuter rail line.
 E1567284 NE FINISHED

How this triple was built (4 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: Haengsin Station | Statement: [Gyeongui–Jungang Line, hasStation, Haengsin Station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Haengsin Station
Context triple: [Gyeongui–Jungang Line, hasStation, Haengsin Station]
  • A. Sangmu Station
    Sangmu Station is a subway station on the Gwangju Metro system in Gwangju, South Korea, serving the Sangmu district.
  • B. Banghwa Station
    Banghwa Station is a subway station in Seoul, South Korea, serving as a key western endpoint on the city's metro network.
  • C. Sangnoksu Station
    Sangnoksu Station is a subway station in Ansan, South Korea, serving as part of the Seoul Metropolitan Subway network.
  • D. Hyehwa Station
    Hyehwa Station is a Seoul Metropolitan Subway station serving the lively Daehangno theater and university district in central Seoul.
  • E. Kwangmyong Station
    Kwangmyong Station is a stop on the Pyongyang Metro system in North Korea’s capital city.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Haengsin Station
Triple: [Gyeongui–Jungang Line, hasStation, Haengsin Station]
Generated description
Haengsin Station is a railway station in South Korea that serves as a stop on Seoul’s Gyeongui–Jungang commuter rail line.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Haengsin Station
Target entity description: Haengsin Station is a railway station in South Korea that serves as a stop on Seoul’s Gyeongui–Jungang commuter rail line.
  • A. Sangmu Station
    Sangmu Station is a subway station on the Gwangju Metro system in Gwangju, South Korea, serving the Sangmu district.
  • B. Banghwa Station
    Banghwa Station is a subway station in Seoul, South Korea, serving as a key western endpoint on the city's metro network.
  • C. Sangnoksu Station
    Sangnoksu Station is a subway station in Ansan, South Korea, serving as part of the Seoul Metropolitan Subway network.
  • D. Hyehwa Station
    Hyehwa Station is a Seoul Metropolitan Subway station serving the lively Daehangno theater and university district in central Seoul.
  • E. Kwangmyong Station
    Kwangmyong Station is a stop on the Pyongyang Metro system in North Korea’s capital city.
  • F. None of above. chosen

Provenance (5 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_69d8e517ebd48190979ee76723bcfadf completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e6515b4d308190af3be1787fa7c65b completed April 20, 2026, 4:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0bf28a68988190bb25d84337226982 completed May 19, 2026, 5:18 a.m.
NEDg Description generation batch_6a0bfbeb1b248190932b5f98f8a91c14 completed May 19, 2026, 5:58 a.m.
NED2 Entity disambiguation (via description) batch_6a0bfc4db40c8190bb5ae34d179a2892 completed May 19, 2026, 5:59 a.m.
Created at: April 10, 2026, 1:47 p.m.