Triple
T12191761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stellingen station |
E290479
|
entity |
| Predicate | hasPassengerSuburbanService |
P68952
|
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: [Stellingen station, hasPassengerSuburbanService, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerSuburbanService Context triple: [Stellingen station, hasPassengerSuburbanService, yes]
-
A.
hasSuburbanService
chosen
Indicates that an entity provides or is connected to a public transportation service specifically serving suburban areas, typically linking suburbs with urban centers.
-
B.
hasBusServices
Indicates that one location or entity is served by bus routes or bus transportation provided by another.
-
C.
hasPublicTransitServiceLevel
Indicates the level or quality of public transit service provided to or available at a given location or entity.
-
D.
hasFormOfPublicTransit
Indicates that one entity provides or is associated with a particular type or mode of public transportation for another entity or context.
-
E.
hasSuburbanServiceBrand
Indicates that an entity operates or is associated with a specific brand used for its suburban transport services.
- 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_69d6ab64de5881908d56eb7a75c6cc69 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d938cd2edc8190b1971349dbc0dee0 |
completed | April 10, 2026, 5:52 p.m. |
| PD | Predicate disambiguation | batch_69d91c38321c819080d500d0d64a04f6 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:50 p.m.