Triple
T30446405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AIXI |
E774590
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | universal artificial intelligence model |
C57010
|
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: universal artificial intelligence model Context triple: [AIXI, instanceOf, universal artificial intelligence model]
-
A.
artificial intelligence
Artificial intelligence is a field of computer science focused on creating systems that can perform tasks that typically require human intelligence, such as learning, reasoning, perception, and decision-making.
-
B.
large-scale model
A large-scale model is a computational model, often in machine learning or simulation, that operates with vast numbers of parameters or variables to capture complex patterns or behaviors across extensive datasets or systems.
-
C.
multimodal large language model family
A multimodal large language model family is a group of related neural models that can jointly process and generate multiple data modalities—such as text, images, audio, or video—using shared architectures, training objectives, and parameterizations.
-
D.
artificial intelligence framework
An artificial intelligence framework is a structured software environment that provides tools, libraries, and interfaces to design, train, deploy, and manage AI and machine learning models efficiently.
-
E.
AI research tool
An AI research tool is a software system that leverages artificial intelligence techniques to assist in discovering, organizing, analyzing, and generating scientific knowledge and insights.
- 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_69f22493ef9c8190ae8c2afcb7f994c8 |
completed | April 29, 2026, 3:32 p.m. |
Created at: April 29, 2026, 8:08 p.m.