Triple
T19734413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gyeongui–Jungang Line |
E473938
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Jipyeong Station |
—
|
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: Jipyeong Station | Statement: [Gyeongui–Jungang Line, hasStation, Jipyeong Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jipyeong Station Context triple: [Gyeongui–Jungang Line, hasStation, Jipyeong Station]
-
A.
Jipyeong Station
chosen
Jipyeong Station is a railway station in South Korea that serves as the eastern endpoint of Seoul’s Gyeongui–Jungang commuter rail line.
-
B.
Myeongnyun Station
Myeongnyun Station is a metro station in Busan, South Korea, serving the Dongnae District on the Busan Metro network.
-
C.
Jeongja Station
Jeongja Station is a major subway station in Seongnam, South Korea, serving as a key hub that connects the Shinbundang Line with other local transit routes.
-
D.
Hapjeong Station
Hapjeong Station is a major Seoul Metropolitan Subway interchange station in Mapo-gu, connecting Line 2 and Line 6 near the Hongdae and Mangwon neighborhoods.
-
E.
Kwangmyong Station
Kwangmyong Station is a stop on the Pyongyang Metro system in North Korea’s capital city.
- 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_69d8e517ebd48190979ee76723bcfadf |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6515b4d308190af3be1787fa7c65b |
completed | April 20, 2026, 4:16 p.m. |
Created at: April 10, 2026, 1:47 p.m.