Triple
T13824454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 1 (Beijing Subway) |
E332214
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Wanshoulu station
Wanshoulu station is a subway station in Beijing, China, serving passengers on the city's extensive metro network.
|
E1127758
|
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: Wanshoulu station | Statement: [Line 1 (Beijing Subway), hasStation, Wanshoulu station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wanshoulu station Context triple: [Line 1 (Beijing Subway), hasStation, Wanshoulu station]
-
A.
Wanshengwei Station
Wanshengwei Station is a metro station in Guangzhou, China, serving as a transport hub within the Guangzhou Metro network.
-
B.
Weiwuying Station
Weiwuying Station is an underground metro station in Kaohsiung, Taiwan, serving the Weiwuying area and providing access to the nearby National Kaohsiung Center for the Arts.
-
C.
Huoying station
Huoying station is an interchange stop on the Beijing Subway that connects passengers to Line 13 and other transit services in the northern part of the city.
-
D.
Yuzhilu station
Yuzhilu station is a subway station on Beijing's extensive metro network, serving passengers along Line 8.
-
E.
Laojie station
Laojie station is a major interchange and one of the busiest metro stations in Shenzhen, China, serving the city’s central commercial and shopping districts.
- 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: Wanshoulu station Triple: [Line 1 (Beijing Subway), hasStation, Wanshoulu station]
Generated description
Wanshoulu station is a subway station in Beijing, China, serving passengers on the city's extensive metro network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wanshoulu station Target entity description: Wanshoulu station is a subway station in Beijing, China, serving passengers on the city's extensive 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.
Weiwuying Station
Weiwuying Station is an underground metro station in Kaohsiung, Taiwan, serving the Weiwuying area and providing access to the nearby National Kaohsiung Center for the Arts.
-
C.
Huoying station
Huoying station is an interchange stop on the Beijing Subway that connects passengers to Line 13 and other transit services in the northern part of the city.
-
D.
Yuzhilu station
Yuzhilu station is a subway station on Beijing's extensive metro network, serving passengers along Line 8.
-
E.
Laojie station
Laojie station is a major interchange and one of the busiest metro stations in Shenzhen, China, serving the city’s central commercial and shopping districts.
- 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_69d81c5ae7c88190b0dd41bdafeb5999 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0285fb7c8190be4b90bdc0d6fa53 |
completed | April 14, 2026, 9:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe7e72b9f08190a33e8e20541edd21 |
completed | May 9, 2026, 12:23 a.m. |
| NEDg | Description generation | batch_69fe7f9ea5848190af5edd6d2d22c117 |
completed | May 9, 2026, 12:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe7ff91dec8190aa9f0d42a8cd00e0 |
completed | May 9, 2026, 12:29 a.m. |
Created at: April 9, 2026, 10:13 p.m.