Triple
T23743152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Molesey railway station |
E586739
|
entity |
| Predicate | hasPassengerHelpPoints |
P121590
|
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: [Molesey railway station, hasPassengerHelpPoints, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerHelpPoints Context triple: [Molesey railway station, hasPassengerHelpPoints, yes]
-
A.
hasPassengerHandling
Indicates that an entity is responsible for or involved in managing the processes and services related to handling passengers.
-
B.
hasPassengerRole
Indicates that an entity participates in a context or event specifically in the capacity or role of a passenger.
-
C.
hasThroughPassengersWith
Indicates that two transportation segments, services, or locations are connected by passengers who travel through them without starting or ending their journey there.
-
D.
hasPassengerUsageCategory
Indicates the classification of how a passenger-related resource or service is used (e.g., its usage type or category for passengers).
-
E.
hasPassengerAmenity
chosen
Indicates that an entity provides or is equipped with a specific amenity intended for the comfort or convenience of its 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_69e24908efb08190bf755c3a9b91f222 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1bcbbcf988190b56d74af4b126bd8 |
completed | April 29, 2026, 8:09 a.m. |
| PD | Predicate disambiguation | batch_69f155f012808190a4b1cbc155558ade |
completed | April 29, 2026, 12:50 a.m. |
Created at: April 17, 2026, 7:12 p.m.