Triple
T16658724
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xiangshan MRT Station |
E404801
|
entity |
| Predicate | hasChineseName |
P4878
|
FINISHED |
| Object |
象山站
象山站 is a Taipei Metro station on the Red Line that serves as the eastern terminus near Elephant Mountain in Taipei, Taiwan.
|
E1225686
|
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: 象山站 | Statement: [Xiangshan MRT Station, hasChineseName, 象山站]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 象山站 Context triple: [Xiangshan MRT Station, hasChineseName, 象山站]
-
A.
Shaoxing North Railway Station
Shaoxing North Railway Station is a major high-speed rail hub serving the city of Shaoxing in Zhejiang Province, China.
-
B.
萱島駅
萱島駅は大阪府寝屋川市に位置し、京阪本線が乗り入れる市内有数の主要ターミナル駅です。
-
C.
Shaoxing Railway Station
Shaoxing Railway Station is a major passenger rail hub serving the city of Shaoxing in Zhejiang Province, China, connecting it to regional and national rail networks.
-
D.
Shichang station
Shichang station is a Beijing Subway station serving as the western terminus of the S1 Line in Beijing, China.
-
E.
Baishizhou station
Baishizhou station is a metro station in Shenzhen, China, serving the densely populated Baishizhou area and nearby attractions such as Window of the World.
- 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: 象山站 Triple: [Xiangshan MRT Station, hasChineseName, 象山站]
Generated description
象山站 is a Taipei Metro station on the Red Line that serves as the eastern terminus near Elephant Mountain in Taipei, Taiwan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 象山站 Target entity description: 象山站 is a Taipei Metro station on the Red Line that serves as the eastern terminus near Elephant Mountain in Taipei, Taiwan.
-
A.
Shaoxing North Railway Station
Shaoxing North Railway Station is a major high-speed rail hub serving the city of Shaoxing in Zhejiang Province, China.
-
B.
萱島駅
萱島駅は大阪府寝屋川市に位置し、京阪本線が乗り入れる市内有数の主要ターミナル駅です。
-
C.
Shaoxing Railway Station
Shaoxing Railway Station is a major passenger rail hub serving the city of Shaoxing in Zhejiang Province, China, connecting it to regional and national rail networks.
-
D.
Shichang station
Shichang station is a Beijing Subway station serving as the western terminus of the S1 Line in Beijing, China.
-
E.
Baishizhou station
Baishizhou station is a metro station in Shenzhen, China, serving the densely populated Baishizhou area and nearby attractions such as Window of the World.
- 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_69d8838b5fbc81908c6575c132b82e80 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37bfcbb6881909c0419174dd017dc |
completed | April 18, 2026, 12:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0084ccbc888190816cdf0ea67b0a90 |
completed | May 10, 2026, 1:14 p.m. |
| NEDg | Description generation | batch_6a008576b0bc81909fdf0b7d26f4c2c1 |
completed | May 10, 2026, 1:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0085e4b6ec81908383085ff08f0dce |
completed | May 10, 2026, 1:19 p.m. |
Created at: April 10, 2026, 5:18 a.m.