Triple
T16124217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tamsui–Xinyi line |
E391223
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Zhongyi station
Zhongyi station is a metro station on the Taipei Metro system in New Taipei City, Taiwan.
|
E1320042
|
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: Zhongyi station | Statement: [Tamsui–Xinyi line, hasStation, Zhongyi station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zhongyi station Context triple: [Tamsui–Xinyi line, hasStation, Zhongyi station]
-
A.
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.
-
B.
Yuzhilu station
Yuzhilu station is a subway station on Beijing's extensive metro network, serving passengers along Line 8.
-
C.
Wanshengwei Station
Wanshengwei Station is a metro station in Guangzhou, China, serving as a transport hub within the Guangzhou Metro network.
-
D.
Huoying station
Huoying station is an interchange stop on the Beijing Subway that connects passengers to Line 13 and other transit services in the northern part of the city.
-
E.
Zuoying Station
Zuoying Station is a major transportation hub in Kaohsiung, Taiwan, serving high-speed rail, conventional rail, and metro services.
- 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: Zhongyi station Triple: [Tamsui–Xinyi line, hasStation, Zhongyi station]
Generated description
Zhongyi station is a metro station on the Taipei Metro system in New Taipei City, Taiwan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zhongyi station Target entity description: Zhongyi station is a metro station on the Taipei Metro system in New Taipei City, Taiwan.
-
A.
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.
-
B.
Yuzhilu station
Yuzhilu station is a subway station on Beijing's extensive metro network, serving passengers along Line 8.
-
C.
Wanshengwei Station
Wanshengwei Station is a metro station in Guangzhou, China, serving as a transport hub within the Guangzhou Metro network.
-
D.
Huoying station
Huoying station is an interchange stop on the Beijing Subway that connects passengers to Line 13 and other transit services in the northern part of the city.
-
E.
Zuoying Station
Zuoying Station is a major transportation hub in Kaohsiung, Taiwan, serving high-speed rail, conventional rail, and metro services.
- 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_69d87f1bb0988190b490d273dbf3fd03 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e2020342988190add65c784b8ee179 |
completed | April 17, 2026, 9:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03cc075a1881909fef72945e47d68a |
completed | May 13, 2026, 12:55 a.m. |
| NEDg | Description generation | batch_6a03cfe2e23c8190bc49aa82debf5367 |
completed | May 13, 2026, 1:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03d04dd0a88190b308c4a2c649f687 |
completed | May 13, 2026, 1:13 a.m. |
Created at: April 10, 2026, 5 a.m.