Triple
T6810252
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daejeon Metro |
E156610
|
entity |
| Predicate | connectsWith |
P37
|
FINISHED |
| Object | Daejeon Station |
E152149
|
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: Daejeon Station | Statement: [Daejeon Metro, connectsWith, Daejeon Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daejeon Station Context triple: [Daejeon Metro, connectsWith, Daejeon Station]
-
A.
Daejeon Station
chosen
Daejeon Station is a major railway hub in central South Korea, serving high-speed KTX trains and connecting Daejeon to key cities nationwide.
-
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.
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.
-
D.
Busan Station
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.
-
E.
Seoul Station
Seoul Station is a major railway and transportation hub in central Seoul, South Korea, serving high-speed, intercity, and commuter trains as well as multiple subway lines.
- 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_69c68828b26c819090fe9df7612bbc27 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d30ded6481908fd64611607c610e |
completed | March 27, 2026, 6:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7425a26d88190ab1e3de2e5596108 |
completed | March 28, 2026, 2:52 a.m. |
Created at: March 27, 2026, 2:16 p.m.