Triple
T19117247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 6 |
E467936
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Seokgye 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: Seokgye Station | Statement: [Line 6, hasStation, Seokgye Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seokgye Station Context triple: [Line 6, hasStation, Seokgye Station]
-
A.
Kwangmyong Station
Kwangmyong Station is a stop on the Pyongyang Metro system in North Korea’s capital city.
-
B.
Myeongnyun Station
Myeongnyun Station is a metro station in Busan, South Korea, serving the Dongnae District on the Busan Metro network.
-
C.
Gwangmyeong Station
Gwangmyeong Station is a major high-speed rail station in Gwangmyeong, South Korea, serving as an important stop on the KTX network connecting Seoul with other key cities nationwide.
-
D.
Cheonggu Station
chosen
Cheonggu Station is a subway station on Seoul’s extensive metropolitan rail network, serving passengers on Line 6.
-
E.
Yeonsu Station
Yeonsu Station is a subway station in Incheon, South Korea, serving the Yeonsu District on the Incheon Subway Line 1.
- 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.