Triple
T3227416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 8 (Beijing Subway) |
E67656
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Zhuxinzhuang depot
Zhuxinzhuang depot is a facility on the Beijing Subway network used for the storage, maintenance, and dispatch of trains serving Line 8.
|
E337411
|
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: Zhuxinzhuang depot | Statement: [Line 8 (Beijing Subway), hasStation, Zhuxinzhuang depot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zhuxinzhuang depot Context triple: [Line 8 (Beijing Subway), hasStation, Zhuxinzhuang depot]
-
A.
Wanshengwei Depot
Wanshengwei Depot is a major operations and maintenance facility serving Guangzhou Metro’s urban rail network in Guangzhou, China.
-
B.
Sanyuanqiao depot
Sanyuanqiao depot is a maintenance and storage facility serving Beijing’s Capital Airport Express line.
-
C.
Tuqiao Depot
Tuqiao Depot is a maintenance and storage facility serving trains on Beijing’s Batong Line of the subway system.
-
D.
Xilang Depot
Xilang Depot is a maintenance and storage facility serving the Guangzhou Metro system in Guangzhou, China.
-
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: Zhuxinzhuang depot Triple: [Line 8 (Beijing Subway), hasStation, Zhuxinzhuang depot]
Generated description
Zhuxinzhuang depot is a facility on the Beijing Subway network used for the storage, maintenance, and dispatch of trains serving Line 8.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zhuxinzhuang depot Target entity description: Zhuxinzhuang depot is a facility on the Beijing Subway network used for the storage, maintenance, and dispatch of trains serving Line 8.
-
A.
Wanshengwei Depot
Wanshengwei Depot is a major operations and maintenance facility serving Guangzhou Metro’s urban rail network in Guangzhou, China.
-
B.
Sanyuanqiao depot
Sanyuanqiao depot is a maintenance and storage facility serving Beijing’s Capital Airport Express line.
-
C.
Tuqiao Depot
Tuqiao Depot is a maintenance and storage facility serving trains on Beijing’s Batong Line of the subway system.
-
D.
Xilang Depot
Xilang Depot is a maintenance and storage facility serving the Guangzhou Metro system in Guangzhou, China.
-
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_69ad858c61888190a31196310d9b30b5 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaeb5e67c819082070d108d3613ba |
completed | March 8, 2026, 5:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b26262af848190a918f3a606bfa616 |
completed | March 12, 2026, 6:51 a.m. |
| NEDg | Description generation | batch_69b264e25bd48190978a289565854297 |
completed | March 12, 2026, 7:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b265cd3fcc8190bc56bbf2de229386 |
completed | March 12, 2026, 7:05 a.m. |
Created at: March 8, 2026, 3:08 p.m.