Triple
T10034836
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peak 2 Peak Gondola |
E204939
|
entity |
| Predicate | travelTimeOneWay |
P46906
|
FINISHED |
| Object | approximately 11 minutes |
—
|
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: approximately 11 minutes | Statement: [Peak 2 Peak Gondola, travelTimeOneWay, approximately 11 minutes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelTimeOneWay Context triple: [Peak 2 Peak Gondola, travelTimeOneWay, approximately 11 minutes]
-
A.
travelTimeTypical
chosen
Indicates the usual or expected amount of time it takes to travel between two locations under normal conditions.
-
B.
travelTimeCategory
Indicates the qualitative classification of how long a given travel or trip duration is (e.g., short, medium, long).
-
C.
approximateDrivingTime
Indicates the estimated amount of time it takes to drive from one location to another under typical conditions.
-
D.
travelTimeToAirport
Indicates the amount of time required to travel from a given location to an airport.
-
E.
reducedTravelTimeFrom
Indicates that one entity has caused or experienced a decrease in the amount of time required to travel from a specified origin entity.
- 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_69ca834d77188190ad645e33e8ca3200 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdce4a515c8190baec86d924623b12 |
completed | April 2, 2026, 2:02 a.m. |
| PD | Predicate disambiguation | batch_69cd4b8638508190b22acc65500ec7d6 |
completed | April 1, 2026, 4:44 p.m. |
Created at: March 30, 2026, 8:55 p.m.