Triple
T20943889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Winchcombe railway station |
E515792
|
entity |
| Predicate | hasLinesideStorageAndWorkshops |
P141791
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Winchcombe railway station, hasLinesideStorageAndWorkshops, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLinesideStorageAndWorkshops Context triple: [Winchcombe railway station, hasLinesideStorageAndWorkshops, yes]
-
A.
hasRailYard
Indicates that one entity possesses, contains, or includes a rail yard as part of its facilities or infrastructure.
-
B.
hasSidePlatformCount
Indicates the number of side platforms associated with an entity, such as a station or stop.
-
C.
hasParkingLotOnBothSidesOfTracks
Indicates that there are parking lots located on both sides of the railway tracks at a given location.
-
D.
hasHangars
Indicates that one entity possesses or contains hangars used for housing aircraft or similar vehicles.
-
E.
hasDedicatedStations
Indicates that specific stations are exclusively assigned or reserved for a particular entity or purpose.
- F. None of above. chosen
Provenance (4 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_69e0b4fc13408190b06868df03c5c29b |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6f95838708190978dfa8bc786fb22 |
completed | April 21, 2026, 4:13 a.m. |
| PD | Predicate disambiguation | batch_69e5c9b1bae48190a845165fed1b005e |
completed | April 20, 2026, 6:37 a.m. |
| PDg | Predicate description generation | batch_69e5d53d22d08190bc17ed4bed53804a |
completed | April 20, 2026, 7:26 a.m. |
Created at: April 16, 2026, 12:52 p.m.