Triple
T16535406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suwon |
E401676
|
entity |
| Predicate | hasTransport |
P1298
|
FINISHED |
| Object | Suwon Station |
E1136622
|
NE FINISHED |
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: Suwon Station | Statement: [Suwon, hasTransport, Suwon Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suwon Station Context triple: [Suwon, hasTransport, Suwon Station]
-
A.
Suwon Station
chosen
Suwon Station is a major railway and subway hub in Suwon, South Korea, serving as a key transit, commercial, and regional transportation center.
-
B.
Yeonsan Station
Yeonsan Station is a major transit hub in Busan, South Korea, serving as an important interchange point on the city’s subway network.
-
C.
Songjeong Station
Songjeong Station is a railway station in Busan, South Korea, serving as a convenient transit point for visitors traveling to the nearby coastal area of Songjeong Beach.
-
D.
Oncheonjang Station
Oncheonjang Station is a subway station in Busan, South Korea, serving the Oncheonjang area in Dongnae District and providing access to its hot spring and commercial zones.
-
E.
Seodaejeon Station
Seodaejeon Station is a major railway station in Daejeon, South Korea, serving as an important stop on national rail lines including high-speed services.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d88384bc30819084229e7dcdc39a41 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e345574d88819094548367bf983078 |
completed | April 18, 2026, 8:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a006094dee481908757b84c10d0dc19 |
completed | May 10, 2026, 10:40 a.m. |
Created at: April 10, 2026, 5:15 a.m.