Triple
T15645445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bannan line |
E376164
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Yongchun station
Yongchun station is a metro station on the Taipei Metro system in Taipei, Taiwan.
|
E1170394
|
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: Yongchun station | Statement: [Bannan line, hasStation, Yongchun station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yongchun station Context triple: [Bannan line, hasStation, Yongchun station]
-
A.
Zhichunlu station
Zhichunlu station is a subway station in Beijing that serves as part of the city's extensive urban rail transit network.
-
B.
Longyan railway station
Longyan railway station is a major passenger and freight rail hub serving the city of Longyan in Fujian Province, China.
-
C.
Sihui station
Sihui station is a Beijing Subway interchange station serving as a key transfer point between major urban rail lines in the city.
-
D.
Jiantan Station
Jiantan Station is a Taipei Metro station in Taiwan that serves as a major access point for visitors to the popular Shilin Night Market.
-
E.
Baishizhou station
Baishizhou station is a metro station in Shenzhen, China, serving the densely populated Baishizhou area and nearby attractions such as Window of the World.
- 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: Yongchun station Triple: [Bannan line, hasStation, Yongchun station]
Generated description
Yongchun station is a metro station on the Taipei Metro system in Taipei, Taiwan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yongchun station Target entity description: Yongchun station is a metro station on the Taipei Metro system in Taipei, Taiwan.
-
A.
Zhichunlu station
Zhichunlu station is a subway station in Beijing that serves as part of the city's extensive urban rail transit network.
-
B.
Longyan railway station
Longyan railway station is a major passenger and freight rail hub serving the city of Longyan in Fujian Province, China.
-
C.
Sihui station
Sihui station is a Beijing Subway interchange station serving as a key transfer point between major urban rail lines in the city.
-
D.
Jiantan Station
Jiantan Station is a Taipei Metro station in Taiwan that serves as a major access point for visitors to the popular Shilin Night Market.
-
E.
Baishizhou station
Baishizhou station is a metro station in Shenzhen, China, serving the densely populated Baishizhou area and nearby attractions such as Window of the World.
- 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_69e04ed400ec8190a14a9f7cf3092865 |
completed | April 16, 2026, 2:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff6790f2288190add8ab0bc0f114bf |
completed | May 9, 2026, 4:57 p.m. |
| NEDg | Description generation | batch_69ff6bc27ef481908f5125ab553b5015 |
completed | May 9, 2026, 5:15 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff6c214c7881908ac483dbb8d08f29 |
completed | May 9, 2026, 5:17 p.m. |
Created at: April 10, 2026, 4:15 a.m.