Triple
T695416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cowdenbeath railway station |
E13883
|
entity |
| Predicate | hasPassengerUsage |
P8370
|
FINISHED |
| Object | used by commuters |
—
|
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: used by commuters | Statement: [Cowdenbeath railway station, hasPassengerUsage, used by commuters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerUsage Context triple: [Cowdenbeath railway station, hasPassengerUsage, used by commuters]
-
A.
hasPassengerUsageCategory
chosen
Indicates the classification of how a passenger-related resource or service is used (e.g., its usage type or category for passengers).
-
B.
hasPassengerRole
Indicates that an entity participates in a context or event specifically in the capacity or role of a passenger.
-
C.
passengers
Indicates that one entity is traveling in or being transported by another entity, typically as a non-operating occupant.
-
D.
hasApproxAnnualPassengerUsageRank
Indicates the approximate position or ranking of an entity based on its annual passenger usage compared to similar entities.
-
E.
hasPassengerTerminal
Indicates that one entity possesses or is equipped with a passenger terminal used for boarding, alighting, or handling passengers.
- 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_69a493406c408190957eeec9048a8fb6 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a0c5f51c8190acc4915099e4b384 |
completed | March 1, 2026, 8:25 p.m. |
| PD | Predicate disambiguation | batch_69a49d23e0a08190b08be9d1eff2a1bb |
completed | March 1, 2026, 8:10 p.m. |
Created at: March 1, 2026, 7:36 p.m.