Triple
T4321361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shenzhen Metro Line 1 |
E96522
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Gangxia station
Gangxia station is a metro station in Shenzhen, China, serving as part of the city’s rapid transit network.
|
E494350
|
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: Gangxia station | Statement: [Shenzhen Metro Line 1, hasStation, Gangxia station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gangxia station Context triple: [Shenzhen Metro Line 1, hasStation, Gangxia station]
-
A.
Guanyinsi station
Guanyinsi station is a metro stop on Beijing’s Daxing Airport Express line serving passengers traveling between the city and Beijing Daxing International Airport.
-
B.
Ximen Station
Ximen Station is a major Taipei Metro interchange station in the Ximending shopping and entertainment district of Taipei, Taiwan.
-
C.
Keyi Road station
Keyi Road station is a subway stop on Beijing's extensive metro network serving passengers in the city's urban area.
-
D.
Xicun Station
Xicun Station is a metro station in Guangzhou, China, serving passengers on the Guangzhou Metro network.
-
E.
Guomao station
Guomao station is a major interchange hub on the Beijing Subway, serving the central business district and connecting key metro lines.
- 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: Gangxia station Triple: [Shenzhen Metro Line 1, hasStation, Gangxia station]
Generated description
Gangxia station is a metro station in Shenzhen, China, serving as part of the city’s rapid transit network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gangxia station Target entity description: Gangxia station is a metro station in Shenzhen, China, serving as part of the city’s rapid transit network.
-
A.
Guanyinsi station
Guanyinsi station is a metro stop on Beijing’s Daxing Airport Express line serving passengers traveling between the city and Beijing Daxing International Airport.
-
B.
Ximen Station
Ximen Station is a major Taipei Metro interchange station in the Ximending shopping and entertainment district of Taipei, Taiwan.
-
C.
Keyi Road station
Keyi Road station is a subway stop on Beijing's extensive metro network serving passengers in the city's urban area.
-
D.
Xicun Station
Xicun Station is a metro station in Guangzhou, China, serving passengers on the Guangzhou Metro network.
-
E.
Guomao station
Guomao station is a major interchange hub on the Beijing Subway, serving the central business district and connecting key metro lines.
- 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_69b345422aac81909ddbadae437d122e |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3511608dc8190afe912aa605ecace |
completed | March 12, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beba400cd88190a2348ec3ac6b711e |
completed | March 21, 2026, 3:33 p.m. |
| NEDg | Description generation | batch_69bebcf50f288190a06efc75ecb104a9 |
completed | March 21, 2026, 3:44 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bebd44820081908fc05f5ebd66099f |
completed | March 21, 2026, 3:46 p.m. |
Created at: March 12, 2026, 11:12 p.m.