Triple
T18063145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Window of the World station |
E432228
|
entity |
| Predicate | adjacentStationOnLine1 |
P5707
|
FINISHED |
| Object | Baishizhou station |
—
|
NE NERFINISHED |
How this triple was built (2 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: Baishizhou station | Statement: [Window of the World station, adjacentStationOnLine1, Baishizhou station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baishizhou station Context triple: [Window of the World station, adjacentStationOnLine1, Baishizhou station]
-
A.
Baishizhou station
chosen
Baishizhou station is a metro station in Shenzhen, China, serving the densely populated Baishizhou area and nearby attractions such as Window of the World.
-
B.
Zhishan station
Zhishan station is a metro station on the Taipei Metro system in Taipei, Taiwan.
-
C.
Zhuwei station
Zhuwei station is a metro station in New Taipei, Taiwan, serving passengers on Taipei Metro’s Tamsui–Xinyi line.
-
D.
Qinghu station
Qinghu station is a metro station in Shenzhen, China, serving as the northern endpoint of the city's Line 4 rapid transit route.
-
E.
Yuanshan station
Yuanshan station is a metro station in Taipei, Taiwan, serving passengers on the city’s Tamsui–Xinyi (Red) Line.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9070cac81909fa9473fb1c3f1c7 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4cce5d2188190ba77d06061d6e77a |
completed | April 19, 2026, 12:39 p.m. |
Created at: April 10, 2026, 10:26 a.m.