Triple
T15476448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taoyuan Airport MRT |
E376791
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Sanchong Station |
E1098575
|
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: Sanchong Station | Statement: [Taoyuan Airport MRT, hasStation, Sanchong Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sanchong Station Context triple: [Taoyuan Airport MRT, hasStation, Sanchong Station]
-
A.
Sanchong Station
chosen
Sanchong Station is a metro station in New Taipei, Taiwan, that serves as a key transit point for commuters in the Sanchong District.
-
B.
Sasang Station
Sasang Station is a major railway and subway interchange in Busan, South Korea, serving as a key transit hub for both local and intercity travel.
-
C.
Beomgye Station
Beomgye Station is a subway station in Anyang, South Korea, serving as a local transit hub on the Seoul metropolitan rail network.
-
D.
Dongsu Station
Dongsu Station is a major subway station in Incheon, South Korea, serving as an important transit hub within the city's metro network.
-
E.
Sajik Station
Sajik Station is a subway station in Busan, South Korea, serving the Dongnae District on the Busan Metro 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_69d85cd21dcc81908646251b1c26ea00 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f88a5dc8190a2d7830748e29180 |
completed | April 16, 2026, 1:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff2d093ccc8190aefc355a837c83f4 |
completed | May 9, 2026, 12:48 p.m. |
Created at: April 10, 2026, 3:34 a.m.