Triple
T15645493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhonghe–Xinlu line |
E376165
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Guting station
Guting station is a Taipei Metro interchange station in Taiwan, serving as a transfer point between multiple subway lines.
|
E1262470
|
NE FINISHED |
How this triple was built (4 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: [Zhonghe–Xinlu line, hasStation, Guting station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guting station Context triple: [Zhonghe–Xinlu line, hasStation, Guting station]
-
A.
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.
-
B.
Gangxia station
Gangxia station is a metro station in Shenzhen, China, serving as part of the city’s rapid transit network.
-
C.
Ximen Station
Ximen Station is a major Taipei Metro interchange station in the Ximending shopping and entertainment district of Taipei, Taiwan.
-
D.
Xicun Station
Xicun Station is a metro station in Guangzhou, China, serving passengers on the Guangzhou Metro network.
-
E.
Weiwuying Station
Weiwuying Station is an underground metro station in Kaohsiung, Taiwan, serving the Weiwuying area and providing access to the nearby National Kaohsiung Center for the Arts.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Guting station Triple: [Zhonghe–Xinlu line, hasStation, Guting station]
Generated description
Guting station is a Taipei Metro interchange station in Taiwan, serving as a transfer point between multiple subway lines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Guting station Target entity description: Guting station is a Taipei Metro interchange station in Taiwan, serving as a transfer point between multiple subway lines.
-
A.
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.
-
B.
Gangxia station
Gangxia station is a metro station in Shenzhen, China, serving as part of the city’s rapid transit network.
-
C.
Ximen Station
Ximen Station is a major Taipei Metro interchange station in the Ximending shopping and entertainment district of Taipei, Taiwan.
-
D.
Xicun Station
Xicun Station is a metro station in Guangzhou, China, serving passengers on the Guangzhou Metro network.
-
E.
Weiwuying Station
Weiwuying Station is an underground metro station in Kaohsiung, Taiwan, serving the Weiwuying area and providing access to the nearby National Kaohsiung Center for the Arts.
- F. None of above. chosen
Provenance (5 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_69d85cd1564c8190991adda63bfab4b0 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04ed5b8b081908d7127964eed3b09 |
completed | April 16, 2026, 2:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a018c32b4a88190a07db59965b38890 |
completed | May 11, 2026, 7:58 a.m. |
| NEDg | Description generation | batch_6a018d3091c08190b49af3a2c8ae846c |
completed | May 11, 2026, 8:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a018d8a4d008190bd244c13d192b625 |
completed | May 11, 2026, 8:04 a.m. |
Created at: April 10, 2026, 4:15 a.m.