Triple
T6650965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaohsiung MRT Orange Line |
E150818
|
entity |
| Predicate | depot |
P14646
|
FINISHED |
| Object |
Daliao Depot
Daliao Depot is a maintenance and storage facility serving trains on the Kaohsiung Mass Rapid Transit system in Kaohsiung, Taiwan.
|
E609814
|
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: Daliao Depot | Statement: [Kaohsiung MRT Orange Line, depot, Daliao Depot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daliao Depot Context triple: [Kaohsiung MRT Orange Line, depot, Daliao Depot]
-
A.
Tuqiao Depot
Tuqiao Depot is a maintenance and storage facility serving trains on Beijing’s Batong Line of the subway system.
-
B.
Wanshengwei Depot
Wanshengwei Depot is a major operations and maintenance facility serving Guangzhou Metro’s urban rail network in Guangzhou, China.
-
C.
Zhuxinzhuang depot
Zhuxinzhuang depot is a facility on the Beijing Subway network used for the storage, maintenance, and dispatch of trains serving Line 8.
-
D.
Sanyuanqiao depot
Sanyuanqiao depot is a maintenance and storage facility serving Beijing’s Capital Airport Express line.
-
E.
Jiahewanggang Depot
Jiahewanggang Depot is a major operations and maintenance facility serving Guangzhou Metro’s urban rail network in Guangzhou, China.
- 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: Daliao Depot Triple: [Kaohsiung MRT Orange Line, depot, Daliao Depot]
Generated description
Daliao Depot is a maintenance and storage facility serving trains on the Kaohsiung Mass Rapid Transit system in Kaohsiung, Taiwan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Daliao Depot Target entity description: Daliao Depot is a maintenance and storage facility serving trains on the Kaohsiung Mass Rapid Transit system in Kaohsiung, Taiwan.
-
A.
Tuqiao Depot
Tuqiao Depot is a maintenance and storage facility serving trains on Beijing’s Batong Line of the subway system.
-
B.
Wanshengwei Depot
Wanshengwei Depot is a major operations and maintenance facility serving Guangzhou Metro’s urban rail network in Guangzhou, China.
-
C.
Zhuxinzhuang depot
Zhuxinzhuang depot is a facility on the Beijing Subway network used for the storage, maintenance, and dispatch of trains serving Line 8.
-
D.
Sanyuanqiao depot
Sanyuanqiao depot is a maintenance and storage facility serving Beijing’s Capital Airport Express line.
-
E.
Jiahewanggang Depot
Jiahewanggang Depot is a major operations and maintenance facility serving Guangzhou Metro’s urban rail network in Guangzhou, China.
- 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_69c687f2c9508190a60b9aad31d3f358 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b0458fb48190a76d8d1d6273a92b |
completed | March 27, 2026, 4:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6eefb3b6c8190ba797dc51966e3a5 |
completed | March 27, 2026, 8:56 p.m. |
| NEDg | Description generation | batch_69c6f1ed97248190ab2253b3b2457f4f |
completed | March 27, 2026, 9:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6f2c18c0081908958b7ffeed7a787 |
completed | March 27, 2026, 9:12 p.m. |
Created at: March 27, 2026, 2:01 p.m.