Triple
T9912571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xilang Depot |
E185780
|
entity |
| Predicate | hasChineseName |
P4878
|
FINISHED |
| Object | 西塱车辆段 |
E185780
|
NE FINISHED |
How this triple was built (2 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: 西塱车辆段 | Statement: [Xilang Depot, hasChineseName, 西塱车辆段]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 西塱车辆段 Context triple: [Xilang Depot, hasChineseName, 西塱车辆段]
-
A.
Jiangwan Town depot
Jiangwan Town depot is a maintenance and storage facility serving Shanghai Metro Line 3 in Shanghai, China.
-
B.
Xilang Depot
chosen
Xilang Depot is a maintenance and storage facility serving the Guangzhou Metro system in Guangzhou, China.
-
C.
Sanyuanqiao depot
Sanyuanqiao depot is a maintenance and storage facility serving Beijing’s Capital Airport Express line.
-
D.
Panyu Square station
Panyu Square station is a metro station in Guangzhou, China, functioning as a stop on the busy Guangzhou Metro Line 3 and serving the Panyu District area.
-
E.
Wanshengwei Depot
Wanshengwei Depot is a major operations and maintenance facility serving Guangzhou Metro’s urban rail network in Guangzhou, China.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca829b45f481909040f7b99a1976ed |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb5391b8081908094b88cdde4b55a |
completed | April 2, 2026, 12:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d20dcbf28c8190aa9f6a8be423670a |
completed | April 5, 2026, 7:22 a.m. |
Created at: March 30, 2026, 8:41 p.m.