Triple
T20874096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suspended Looping Coaster |
E513971
|
entity |
| Predicate | riderExperience |
P118952
|
FINISHED |
| Object | legs dangling below the seat |
—
|
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: legs dangling below the seat | Statement: [Suspended Looping Coaster, riderExperience, legs dangling below the seat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: riderExperience Context triple: [Suspended Looping Coaster, riderExperience, legs dangling below the seat]
-
A.
intendedRiderExperience
chosen
Indicates the type or quality of experience that is planned or designed for a rider in the context of a ride or transportation service.
-
B.
riderType
Indicates the category or role of a rider in relation to a ride, transport service, or vehicle (e.g., passenger, driver, courier).
-
C.
ridershipLevel
Indicates the magnitude or intensity of usage by riders or passengers for a given service, route, or system.
-
D.
hasRideExperience
Indicates that one entity has undergone, participated in, or possesses experience with a particular ride or riding activity in relation to another entity.
-
E.
drivingExperience
Indicates the extent or history of a person's involvement in driving vehicles, typically measured by duration, frequency, or level of skill.
- 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_69e0b4f675cc8190b4e745225b62eb66 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c46639308190a616193f3975d453 |
completed | April 21, 2026, 12:27 a.m. |
| PD | Predicate disambiguation | batch_69e5c9a8dc148190b33ff51894e2a8f9 |
completed | April 20, 2026, 6:37 a.m. |
Created at: April 16, 2026, 12:45 p.m.