Triple
T19701846
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dorasan Station |
E473116
|
entity |
| Predicate | operator |
P179
|
FINISHED |
| Object | Korail |
—
|
NE NERFINISHED |
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: Korail | Statement: [Dorasan Station, operator, Korail]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Korail Context triple: [Dorasan Station, operator, Korail]
-
A.
Korail
chosen
Korail is South Korea's national railroad operator, managing the country's major passenger and freight rail services.
-
B.
KTX
KTX is a Khronos Group-defined container format for efficiently storing and transmitting GPU-ready texture data in graphics applications.
-
C.
KTX
KTX is South Korea’s high-speed rail service that connects major cities such as Seoul and Busan.
-
D.
Hanwa Line
The Hanwa Line is a major railway line in Japan’s Kansai region operated by JR West, connecting central Osaka with southern Osaka Prefecture and Wakayama.
-
E.
Saemaeul-ho express
Saemaeul-ho express was a long-distance passenger train service in South Korea that operated as one of the country’s primary intercity rail options before being succeeded by the ITX-Saemaeul.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e515bef88190bc30781aea50537a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e642b667908190841bb5fb7bfdb3f7 |
completed | April 20, 2026, 3:13 p.m. |
Created at: April 10, 2026, 1:46 p.m.