Triple
T15645437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bannan line |
E376164
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Ximen station |
E480441
|
NE FINISHED |
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: Ximen station | Statement: [Bannan line, hasStation, Ximen station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ximen station Context triple: [Bannan line, hasStation, Ximen station]
-
A.
Ximen Station
chosen
Ximen Station is a major Taipei Metro interchange station in the Ximending shopping and entertainment district of Taipei, Taiwan.
-
B.
Xicun Station
Xicun Station is a metro station in Guangzhou, China, serving passengers on the Guangzhou Metro network.
-
C.
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.
-
D.
Xitucheng station
Xitucheng station is a subway station on the Beijing Subway system serving the Haidian District area.
-
E.
Gangxia station
Gangxia station is a metro station in Shenzhen, China, serving as part of the city’s rapid transit network.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d85cd1564c8190991adda63bfab4b0 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04ed400ec8190a14a9f7cf3092865 |
completed | April 16, 2026, 2:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01232399008190b23dfaec237563ef |
completed | May 11, 2026, 12:30 a.m. |
Created at: April 10, 2026, 4:15 a.m.