Triple
T1650452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guangzhou Metro |
E35677
|
entity |
| Predicate | hasDepot |
P2413
|
FINISHED |
| Object |
Xilang Depot
Xilang Depot is a maintenance and storage facility serving the Guangzhou Metro system in Guangzhou, China.
|
E185780
|
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: Xilang Depot | Statement: [Guangzhou Metro, hasDepot, Xilang Depot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Xilang Depot Context triple: [Guangzhou Metro, hasDepot, Xilang Depot]
-
A.
Guomao station
Guomao station is a major interchange hub on the Beijing Subway, serving the central business district and connecting key metro lines.
-
B.
Pingguoyuan station
Pingguoyuan station is a major western terminus and interchange station on the Beijing Subway serving the Shijingshan District of Beijing, China.
-
C.
Sihui station
Sihui station is a Beijing Subway interchange station serving as a key transfer point between major urban rail lines in the city.
-
D.
Zhichunlu station
Zhichunlu station is a subway station in Beijing that serves as part of the city's extensive urban rail transit network.
-
E.
Guangzhou South Railway Station
Guangzhou South Railway Station is a major high-speed rail hub in southern China, serving as one of the country’s busiest and most important transportation centers.
- 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: Xilang Depot Triple: [Guangzhou Metro, hasDepot, Xilang Depot]
Generated description
Xilang Depot is a maintenance and storage facility serving the Guangzhou Metro system in Guangzhou, China.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Xilang Depot Target entity description: Xilang Depot is a maintenance and storage facility serving the Guangzhou Metro system in Guangzhou, China.
-
A.
Guomao station
Guomao station is a major interchange hub on the Beijing Subway, serving the central business district and connecting key metro lines.
-
B.
Pingguoyuan station
Pingguoyuan station is a major western terminus and interchange station on the Beijing Subway serving the Shijingshan District of Beijing, China.
-
C.
Sihui station
Sihui station is a Beijing Subway interchange station serving as a key transfer point between major urban rail lines in the city.
-
D.
Zhichunlu station
Zhichunlu station is a subway station in Beijing that serves as part of the city's extensive urban rail transit network.
-
E.
Guangzhou South Railway Station
Guangzhou South Railway Station is a major high-speed rail hub in southern China, serving as one of the country’s busiest and most important transportation centers.
- 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_69a8860568888190a32cd9f70acbba42 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90a66b58c819082d38ef1c805cf44 |
completed | March 5, 2026, 4:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad60a996508190bc227400cb7713ac |
completed | March 8, 2026, 11:42 a.m. |
| NEDg | Description generation | batch_69ad61323b308190b883c4bf2c3ca1bf |
completed | March 8, 2026, 11:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad622d695481909351a9c80f8d646f |
completed | March 8, 2026, 11:49 a.m. |
Created at: March 4, 2026, 7:29 p.m.