Triple
T16652517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taipei Metro Bannan Line |
E404640
|
entity |
| Predicate | depot |
P14646
|
FINISHED |
| Object |
Nangang Depot
Nangang Depot is a major maintenance and storage facility serving Taipei's metro system in the Nangang District.
|
E1226613
|
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: Nangang Depot | Statement: [Taipei Metro Bannan Line, depot, Nangang Depot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nangang Depot Context triple: [Taipei Metro Bannan Line, depot, Nangang Depot]
-
A.
Munyang Depot
Munyang Depot is a maintenance and storage facility serving the Daegu Metro system in Daegu, South Korea.
-
B.
Subang Depot
Subang Depot is a maintenance and storage facility serving trains on the LRT Kelana Jaya Line in the Klang Valley rail network of Malaysia.
-
C.
Fukagawa Depot
Fukagawa Depot is a major Tokyo Metro maintenance and storage facility serving trains on the Tozai Line.
-
D.
Kikuna Depot
Kikuna Depot is a railway maintenance and storage facility in Yokohama that services Tokyo Metro rolling stock such as the 8000 series trains.
-
E.
Oji Depot
Oji Depot is a Tokyo Metro rail yard and maintenance facility serving trains on the Namboku Line in Tokyo, Japan.
- 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: Nangang Depot Triple: [Taipei Metro Bannan Line, depot, Nangang Depot]
Generated description
Nangang Depot is a major maintenance and storage facility serving Taipei's metro system in the Nangang District.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nangang Depot Target entity description: Nangang Depot is a major maintenance and storage facility serving Taipei's metro system in the Nangang District.
-
A.
Munyang Depot
Munyang Depot is a maintenance and storage facility serving the Daegu Metro system in Daegu, South Korea.
-
B.
Subang Depot
Subang Depot is a maintenance and storage facility serving trains on the LRT Kelana Jaya Line in the Klang Valley rail network of Malaysia.
-
C.
Fukagawa Depot
Fukagawa Depot is a major Tokyo Metro maintenance and storage facility serving trains on the Tozai Line.
-
D.
Kikuna Depot
Kikuna Depot is a railway maintenance and storage facility in Yokohama that services Tokyo Metro rolling stock such as the 8000 series trains.
-
E.
Oji Depot
Oji Depot is a Tokyo Metro rail yard and maintenance facility serving trains on the Namboku Line in Tokyo, Japan.
- 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_69d8838a41f08190b0c3f79c47df5078 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37bf7ae7c81908b6807acd8f1669b |
completed | April 18, 2026, 12:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0084c4c6a08190874264b2840fc70d |
completed | May 10, 2026, 1:14 p.m. |
| NEDg | Description generation | batch_6a00867b9314819080775dff976f93bd |
completed | May 10, 2026, 1:22 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00874646c081908d8a83dd8fd44941 |
completed | May 10, 2026, 1:25 p.m. |
Created at: April 10, 2026, 5:18 a.m.