Triple
T1650433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guangzhou Metro |
E35677
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Xicun Station
Xicun Station is a metro station in Guangzhou, China, serving passengers on the Guangzhou Metro network.
|
E217774
|
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: Xicun Station | Statement: [Guangzhou Metro, hasStation, Xicun Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Xicun Station Context triple: [Guangzhou Metro, hasStation, Xicun Station]
-
A.
Wanshengwei Station
Wanshengwei Station is a metro station in Guangzhou, China, serving as a transport hub within the Guangzhou Metro network.
-
B.
Changshou Lu Station
Changshou Lu Station is an underground metro station on the Guangzhou Metro system serving the bustling Changshou Road commercial area in Guangzhou, China.
-
C.
Tiyu Xilu Station
Tiyu Xilu Station is a major interchange and one of the busiest metro stations in Guangzhou, China, serving as a key hub in the Guangzhou Metro network.
-
D.
Guomao station
Guomao station is a major interchange hub on the Beijing Subway, serving the central business district and connecting key metro lines.
-
E.
Zhichunlu station
Zhichunlu station is a subway station in Beijing that serves as part of the city's extensive urban rail transit network.
- 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: Xicun Station Triple: [Guangzhou Metro, hasStation, Xicun Station]
Generated description
Xicun Station is a metro station in Guangzhou, China, serving passengers on the Guangzhou Metro network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Xicun Station Target entity description: Xicun Station is a metro station in Guangzhou, China, serving passengers on the Guangzhou Metro network.
-
A.
Wanshengwei Station
Wanshengwei Station is a metro station in Guangzhou, China, serving as a transport hub within the Guangzhou Metro network.
-
B.
Changshou Lu Station
Changshou Lu Station is an underground metro station on the Guangzhou Metro system serving the bustling Changshou Road commercial area in Guangzhou, China.
-
C.
Tiyu Xilu Station
Tiyu Xilu Station is a major interchange and one of the busiest metro stations in Guangzhou, China, serving as a key hub in the Guangzhou Metro network.
-
D.
Guomao station
Guomao station is a major interchange hub on the Beijing Subway, serving the central business district and connecting key metro lines.
-
E.
Zhichunlu station
Zhichunlu station is a subway station in Beijing that serves as part of the city's extensive urban rail transit network.
- 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_69adf3b54b888190889555d51e563742 |
completed | March 8, 2026, 10:09 p.m. |
| NEDg | Description generation | batch_69adf5eb2b908190a485f5d22e28d252 |
completed | March 8, 2026, 10:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adf6883b808190bfe45d8f07c68696 |
completed | March 8, 2026, 10:22 p.m. |
Created at: March 4, 2026, 7:29 p.m.