Triple
T10502505
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple AI/ML organization |
E247705
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | artificial intelligence organization |
C16706
|
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 organization Context triple: [Apple AI/ML organization, instanceOf, artificial intelligence organization]
-
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.
machine learning research institute
A machine learning research institute is an organization dedicated to advancing the theory, algorithms, and applications of machine learning through systematic research, experimentation, and collaboration.
-
C.
information and communications technology organization
chosen
An information and communications technology organization is an entity that develops, manages, or delivers digital and networked technologies, services, and infrastructure to enable electronic communication, data processing, and information exchange.
-
D.
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.
-
E.
organization
An organization is a structured group of people and resources coordinated to achieve shared goals or perform specific functions.
- 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_69d381c4aa948190942e1d803143fb0e |
completed | April 6, 2026, 9:49 a.m. |
Created at: April 6, 2026, 12:25 p.m.