Triple
T19117056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gimpo Goldline |
E467932
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Yangchon 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: Yangchon station | Statement: [Gimpo Goldline, hasStation, Yangchon station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yangchon station Context triple: [Gimpo Goldline, hasStation, Yangchon station]
-
A.
Yangchon station
chosen
Yangchon station is a subway station in Gimpo, South Korea, serving as one end of the Gimpo Goldline.
-
B.
Myeongnyun Station
Myeongnyun Station is a metro station in Busan, South Korea, serving the Dongnae District on the Busan Metro network.
-
C.
Yeonsu Station
Yeonsu Station is a subway station in Incheon, South Korea, serving the Yeonsu District on the Incheon Subway Line 1.
-
D.
Kwangmyong Station
Kwangmyong Station is a stop on the Pyongyang Metro system in North Korea’s capital city.
-
E.
Yangjae Station
Yangjae Station is a major subway station in southern Seoul, South Korea, serving as an important transit hub on multiple lines within the city’s metro network.
- 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_69d8dd06a26481908039e2a1bae8c597 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e399a6d8819090a9501ff1637b9d |
completed | April 20, 2026, 8:28 a.m. |
Created at: April 10, 2026, 12:05 p.m.