Triple
T19599066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AREX airport railroad |
E470419
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Gyeyang 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: Gyeyang Station | Statement: [AREX airport railroad, hasStation, Gyeyang Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gyeyang Station Context triple: [AREX airport railroad, hasStation, Gyeyang Station]
-
A.
Gyeyang Station
chosen
Gyeyang Station is a major transit hub in Incheon, South Korea, serving as an interchange between the Incheon Subway, AREX airport railroad, and local bus routes.
-
B.
Kwangmyong Station
Kwangmyong Station is a stop on the Pyongyang Metro system in North Korea’s capital city.
-
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.
Beomgye Station
Beomgye Station is a subway station in Anyang, South Korea, serving as a local transit hub on the Seoul metropolitan rail network.
-
E.
Myeongnyun Station
Myeongnyun Station is a metro station in Busan, South Korea, serving the Dongnae District on the Busan 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_69d8e510024481908415c0d616fa6186 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e6407d46188190b9818665b2a698a5 |
completed | April 20, 2026, 3:04 p.m. |
Created at: April 10, 2026, 1:43 p.m.