Triple
T32256851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | HRS (High Resolution Stereoscopic) |
E824048
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | stereoscopic imaging sensor |
C53422
|
CONCEPT FINISHED |
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: stereoscopic imaging sensor Context triple: [HRS (High Resolution Stereoscopic), instanceOf, stereoscopic imaging sensor]
-
A.
depth sensor
chosen
A depth sensor is a device that measures the distance from itself to objects in its field of view, typically using technologies like time-of-flight, structured light, or stereo vision to produce depth information.
-
B.
retina-inspired sensor
A retina-inspired sensor is a bio-mimetic imaging device that emulates the structure and processing principles of the human retina to capture and pre-process visual information efficiently and adaptively.
-
C.
camera
A camera is a device that captures still images or moving video by focusing light onto a photosensitive surface, such as film or a digital sensor.
-
D.
speckle imaging camera
A speckle imaging camera is a specialized high-speed imaging device that captures many short-exposure frames to reconstruct high-resolution images by analyzing and processing speckle patterns caused by atmospheric or medium-induced distortions.
-
E.
high-dynamic-range imaging technology
High-dynamic-range imaging technology is a method of capturing, processing, and displaying images with a wider range of luminance and color than standard imaging, preserving detail in both very bright and very dark areas.
- F. None of above.
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_69f3490db0748190bfef6e50c95d39d3 |
completed | April 30, 2026, 12:20 p.m. |
Created at: May 1, 2026, 12:41 a.m.