Triple
T8948913
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Busan Chinatown |
E213292
|
entity |
| Predicate | hasNearbyAttraction |
P2064
|
FINISHED |
| Object | Busan Station |
E36550
|
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: Busan Station | Statement: [Busan Chinatown, hasNearbyAttraction, Busan Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Busan Station Context triple: [Busan Chinatown, hasNearbyAttraction, Busan Station]
-
A.
Busan Station
chosen
Busan Station is a major railway hub in Busan, South Korea, serving high-speed KTX trains and regional services as one of the country’s key transportation centers.
-
B.
Pohang Station
Pohang Station is a major railway station in Pohang, South Korea, serving as the eastern endpoint of high-speed KTX services and a key regional transportation hub.
-
C.
Daegu Station
Daegu Station is a major railway and metro hub in Daegu, South Korea, serving as a key transit point for regional and urban transportation.
-
D.
Gwangju station
Gwangju station is a major railway station in Gwangju, South Korea, serving as a key hub for regional and intercity train services.
-
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_69ca839843408190a39069a029a89f15 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6709c7a48190ab503083a1d6a29f |
completed | April 1, 2026, 12:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc206550c8190abf016f25b14fa64 |
completed | April 3, 2026, 1:35 p.m. |
Created at: March 30, 2026, 6:59 p.m.