Triple
T16124234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tamsui–Xinyi line |
E391223
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Guting station |
—
|
NE NERFINISHED |
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: Guting station | Statement: [Tamsui–Xinyi line, hasStation, Guting station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guting station Context triple: [Tamsui–Xinyi line, hasStation, Guting station]
-
A.
Guting station
chosen
Guting station is a Taipei Metro interchange station in Taiwan, serving as a transfer point between multiple subway lines.
-
B.
Guanyinsi station
Guanyinsi station is a metro stop on Beijing’s Daxing Airport Express line serving passengers traveling between the city and Beijing Daxing International Airport.
-
C.
Gangxia station
Gangxia station is a metro station in Shenzhen, China, serving as part of the city’s rapid transit network.
-
D.
Ximen Station
Ximen Station is a major Taipei Metro interchange station in the Ximending shopping and entertainment district of Taipei, Taiwan.
-
E.
Xicun Station
Xicun Station is a metro station in Guangzhou, China, serving passengers on the Guangzhou Metro network.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d87f1bb0988190b490d273dbf3fd03 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e2020342988190add65c784b8ee179 |
completed | April 17, 2026, 9:48 a.m. |
Created at: April 10, 2026, 5 a.m.