Triple
T19540549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sinsa-dong |
E488886
|
entity |
| Predicate | hasTransportation |
P105
|
FINISHED |
| Object | Sinsa 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: Sinsa station | Statement: [Sinsa-dong, hasTransportation, Sinsa station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sinsa station Context triple: [Sinsa-dong, hasTransportation, Sinsa station]
-
A.
Sinsa station
chosen
Sinsa station is a subway station in Seoul, South Korea, serving the affluent Sinsa-dong area known for its shopping and dining.
-
B.
Sajik Station
Sajik Station is a subway station in Busan, South Korea, serving the Dongnae District on the Busan Metro network.
-
C.
Sadang Station
Sadang Station is a major Seoul Metropolitan Subway transfer station in southern Seoul, serving as an important transit hub for commuters in and around Gwanak-gu.
-
D.
Asan Station
Asan Station is a railway station in Asan, South Korea, serving as a regional transit hub connecting local and intercity rail services.
-
E.
Sujin station
Sujin station is a railway station on the Seoul Metropolitan Subway network serving passengers in the Seongnam area of Gyeonggi Province, South Korea.
- 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_69d8e8db5b6c8190984b61f91981f575 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63872fda48190bbb1f465cb7b57fe |
completed | April 20, 2026, 2:30 p.m. |
Created at: April 10, 2026, 1:41 p.m.