Triple
T8437741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haeundae District |
E199270
|
entity |
| Predicate | hasTransportation |
P105
|
FINISHED |
| Object | Haeundae Station |
E628358
|
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: Haeundae Station | Statement: [Haeundae District, hasTransportation, Haeundae Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haeundae Station Context triple: [Haeundae District, hasTransportation, Haeundae Station]
-
A.
Haeundae Station
chosen
Haeundae Station is a subway station in Busan, South Korea, serving the popular Haeundae Beach area and its surrounding tourist attractions.
-
B.
Seocho Station
Seocho Station is a subway station in southern Seoul, South Korea, serving as a transit hub for commuters in the Seocho area.
-
C.
Yongsan station
Yongsan station is a major railway and subway hub in central Seoul, South Korea, serving high-speed, intercity, and commuter trains as well as multiple metro lines.
-
D.
Yeongdeungpo Station
Yeongdeungpo Station is a major railway and subway interchange in Seoul, South Korea, serving as an important transportation and commercial hub for the Yeongdeungpo area.
-
E.
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.
- 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_69ca8314cd6c8190a6b8c2a1096e18f3 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe13446788190ad52a4fd6e8b498a |
completed | March 31, 2026, 2:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea82225148190b9dd190655114c8a |
completed | April 2, 2026, 5:32 p.m. |
Created at: March 30, 2026, 6:08 p.m.