Triple
T14923568
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GROND |
E371574
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | multi-channel imager |
C10675
|
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: multi-channel imager Context triple: [GROND, instanceOf, multi-channel imager]
-
A.
imager
chosen
An imager is a component or system that captures, generates, or processes visual representations of data, scenes, or objects into image form.
-
B.
multispectral targeting system
A multispectral targeting system is an integrated sensor and processing suite that detects, tracks, and designates targets across multiple parts of the electromagnetic spectrum (e.g., visible, infrared, and radar) to enhance accuracy and situational awareness.
-
C.
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.
-
D.
mobile imaging technology
Mobile imaging technology encompasses portable, often handheld devices and systems that capture, process, and transmit visual or sensor-based images for applications such as diagnostics, surveillance, mapping, and consumer photography.
-
E.
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.
- 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_69d85cc7ea3481908228b5acb7d06f12 |
completed | April 10, 2026, 2:13 a.m. |
Created at: April 10, 2026, 2:34 a.m.