Triple
T5052769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Google Gemini |
E113825
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | generative artificial intelligence model |
C4177
|
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: generative artificial intelligence model Context triple: [Google Gemini, instanceOf, generative artificial intelligence model]
-
A.
deep learning model
chosen
A deep learning model is a computational architecture composed of multiple layers of interconnected processing units (neurons) that automatically learn hierarchical representations from data to perform tasks such as classification, prediction, or generation.
-
B.
foundational artificial intelligence text
A foundational artificial intelligence text is a comprehensive, authoritative work that establishes core theories, methods, and principles of AI, serving as a primary reference for learning and advancing the field.
-
C.
art model
An art model is a person who poses for artists, photographers, or art classes to provide a live reference for studying and depicting the human form, expression, or composition.
-
D.
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.
-
E.
text-to-speech model
A text-to-speech model is a system that converts written text into natural-sounding spoken audio using linguistic analysis and speech synthesis techniques.
- 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_69bd443aa1f88190abb992d138f2cf42 |
completed | March 20, 2026, 12:57 p.m. |
Created at: March 20, 2026, 1:38 p.m.