Triple
T34613402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Camera Recording Its Own Condition (7 apertures, 10 speeds, 2 mirrors) |
E888797
|
entity |
| Predicate | featureCountSpeeds |
—
|
GENERATED |
| Object | 10 |
—
|
UNRECOGNIZED GENERATED |
How this triple was built (1 step)
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.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featureCountSpeeds Context triple: [A Camera Recording Its Own Condition (7 apertures, 10 speeds, 2 mirrors), featureCountSpeeds, 10]
-
A.
speedDescribedAs
Indicates that one entity characterizes or labels the speed of another entity using a particular description or term.
-
B.
hasAverageSurfaceSpeed
Indicates the typical or mean speed at which something moves across a surface over a given period or distance.
-
C.
featuresSample
Indicates that an entity includes or presents a particular sample as one of its components or examples.
-
D.
speedupType
Indicates the kind or category of performance improvement achieved relative to a baseline.
-
E.
designedServiceSpeed
Indicates the intended or specified operational speed at which a service is designed to function.
- F. None of above. chosen
Provenance (1 batch)
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_69f349d584e08190b40b9f6281ad50c4 |
completed | April 30, 2026, 12:23 p.m. |
Created at: May 1, 2026, 2:03 a.m.