Triple
T12635705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beijing West railway station |
E301757
|
entity |
| Predicate | numberOfIslandPlatforms |
P18595
|
FINISHED |
| Object | 9 |
—
|
LITERAL 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: 9 | Statement: [Beijing West railway station, numberOfIslandPlatforms, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfIslandPlatforms Context triple: [Beijing West railway station, numberOfIslandPlatforms, 9]
-
A.
hasIslandPlatforms
chosen
Indicates that the subject has one or more island-style platforms, typically positioned between tracks and accessible from both sides.
-
B.
hasNumberOfPlatforms
Indicates the relationship that specifies how many platforms are associated with a given entity.
-
C.
hasSidePlatformCount
Indicates the number of side platforms associated with an entity, such as a station or stop.
-
D.
hasRailPlatforms
Indicates that an entity is equipped with one or more rail platforms used for boarding or alighting from trains.
-
E.
numberOfStations
Indicates the total count of stations associated with or contained by a given entity.
- F. None of above.
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_69d7bdec9f9c8190b4bac675b7588211 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961ae493481908f82e0d05dce20bd |
completed | April 10, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69d960b47130819097e1162ed4fc993a |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:16 p.m.