Triple
T1634315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Google Lens |
E35328
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | artificial intelligence application |
C4099
|
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: artificial intelligence application Context triple: [Google Lens, instanceOf, artificial intelligence application]
-
A.
enterprise AI product
An enterprise AI product is a scalable, secure software solution that embeds artificial intelligence into business workflows to automate tasks, augment decision-making, and deliver measurable operational and strategic value across an organization.
-
B.
intelligent personal assistant
chosen
An intelligent personal assistant is a software agent that uses artificial intelligence to understand user requests, manage tasks, and provide personalized information or services through natural language interaction.
-
C.
machine learning framework
A machine learning framework is a software library or platform that provides tools, abstractions, and workflows to design, train, evaluate, and deploy machine learning models efficiently.
-
D.
algorithm
An algorithm is a finite, well-defined sequence of computational steps or rules designed to solve a specific problem or perform a particular task.
-
E.
game-playing AI
A game-playing AI is an artificial intelligence system designed to analyze game states, make strategic decisions, and execute actions to achieve optimal performance or victory within a defined set of game rules.
- 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_69a886036bc081909ff5de16dbe5e8ea |
completed | March 4, 2026, 7:20 p.m. |
Created at: March 4, 2026, 7:28 p.m.