Triple
T7387147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | XeSS |
E170408
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | spatial-temporal upscaling algorithm |
C9940
|
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: spatial-temporal upscaling algorithm Context triple: [XeSS, instanceOf, spatial-temporal upscaling algorithm]
-
A.
image upscaling technology
chosen
Image upscaling technology is a set of algorithms and tools that increase the resolution and apparent quality of digital images by intelligently adding or refining pixel data, often using advanced methods like machine learning or deep learning.
-
B.
global data-processing and forecasting system
A global data-processing and forecasting system is an integrated platform that ingests, cleans, analyzes, and models large-scale, heterogeneous data from worldwide sources to generate timely predictions and insights for decision-making.
-
C.
spatiotemporal rift
A spatiotemporal rift is a localized disruption in the fabric of space and time that creates an anomalous region where normal physical laws, positions, and temporal sequences are distorted or discontinuous.
-
D.
static spacetime
A static spacetime is a spacetime that admits a global timelike Killing vector field that is hypersurface-orthogonal, so its geometry is time-independent and free of rotation.
-
E.
spatial computing platform
A spatial computing platform is an integrated hardware and software environment that blends digital content with the physical world, enabling users to interact with 3D information and experiences in real space.
- 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_69c68a5e2c9081909e713ce866e0060a |
completed | March 27, 2026, 1:47 p.m. |
Created at: March 27, 2026, 3:08 p.m.