Triple
T8177180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ITX-Saemaeul |
E190965
|
entity |
| Predicate | primaryRoute |
P6298
|
FINISHED |
| Object | Seoul–Pohang |
E716382
|
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: Seoul–Pohang | Statement: [ITX-Saemaeul, primaryRoute, Seoul–Pohang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seoul–Pohang Context triple: [ITX-Saemaeul, primaryRoute, Seoul–Pohang]
-
A.
Seoul–Busan
chosen
Seoul–Busan refers to the major intercity corridor in South Korea connecting the capital Seoul with the southeastern port city of Busan, one of the country’s busiest and most important travel routes.
-
B.
Pohang
Pohang is a major industrial and port city in South Korea, best known as the home of the global steelmaker POSCO and a key hub on the country’s east coast.
-
C.
Siheung
Siheung is a coastal city in northwestern South Korea known for its industrial complexes, wetlands, and proximity to Seoul.
-
D.
Pyeongtaek
Pyeongtaek is a South Korean city in Gyeonggi Province known for its major U.S. and UN military presence, including large bases such as Camp Humphreys.
-
E.
Daejeon-yeok
Daejeon-yeok is the romanized Korean name for Daejeon Station, a major railway hub in the city of Daejeon, South Korea.
- 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_69ca82c1c0a08190bf8692b4d91a03ca |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb4aba0dd88190828080d0d89612eb |
completed | March 31, 2026, 4:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce1cb9c9e0819080ea2875b22537ec |
completed | April 2, 2026, 7:37 a.m. |
Created at: March 30, 2026, 5:40 p.m.