Triple
T15665354
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wenhu line |
E377171
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Gangqian station
Gangqian station is a metro station on Taipei's Wenhu (Brown) Line serving the Neihu District.
|
E1284631
|
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: Gangqian station | Statement: [Wenhu line, hasStation, Gangqian station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gangqian station Context triple: [Wenhu line, hasStation, Gangqian station]
-
A.
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.
-
B.
Guomao station
Guomao station is a major interchange hub on the Beijing Subway, serving the central business district and connecting key metro lines.
-
C.
Gucheng station
Gucheng station is a subway station in Beijing that serves passengers on the city’s Line 1 metro route.
-
D.
Zuoying Station
Zuoying Station is a major transportation hub in Kaohsiung, Taiwan, serving high-speed rail, conventional rail, and metro services.
-
E.
Laojie station
Laojie station is a major interchange and one of the busiest metro stations in Shenzhen, China, serving the city’s central commercial and shopping districts.
- 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: Gangqian station Triple: [Wenhu line, hasStation, Gangqian station]
Generated description
Gangqian station is a metro station on Taipei's Wenhu (Brown) Line serving the Neihu District.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gangqian station Target entity description: Gangqian station is a metro station on Taipei's Wenhu (Brown) Line serving the Neihu District.
-
A.
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.
-
B.
Guomao station
Guomao station is a major interchange hub on the Beijing Subway, serving the central business district and connecting key metro lines.
-
C.
Gucheng station
Gucheng station is a subway station in Beijing that serves passengers on the city’s Line 1 metro route.
-
D.
Zuoying Station
Zuoying Station is a major transportation hub in Kaohsiung, Taiwan, serving high-speed rail, conventional rail, and metro services.
-
E.
Laojie station
Laojie station is a major interchange and one of the busiest metro stations in Shenzhen, China, serving the city’s central commercial and shopping districts.
- 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_69d85cd2e28481909d4e975bee20872f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04f0f4df08190ad2c5d78e435d8eb |
completed | April 16, 2026, 2:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a023005e760819081b723a7246c78ce |
completed | May 11, 2026, 7:37 p.m. |
| NEDg | Description generation | batch_6a023121d7b88190bd832d41f47c3fdd |
completed | May 11, 2026, 7:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a02357111388190a0c7bc7ae7ca1a1b |
completed | May 11, 2026, 8 p.m. |
Created at: April 10, 2026, 4:16 a.m.