Triple
T22412013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DNNresearch Inc. |
E554014
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | deep learning company |
C32995
|
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: deep learning company Context triple: [DNNresearch Inc., instanceOf, deep learning company]
-
A.
Data and AI company
chosen
A Data and AI company specializes in collecting, managing, and analyzing data to build intelligent systems and solutions that automate decisions, generate insights, and create predictive or generative capabilities for businesses.
-
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.
deep learning library
A deep learning library is a software framework that provides tools, abstractions, and optimized routines to design, train, and deploy neural network models.
-
D.
deep learning model
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.
-
E.
machine learning division
The machine learning division is an organizational unit responsible for researching, developing, and deploying data-driven algorithms and models to solve complex problems and enhance products or services.
- 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_69e11e4e6ce8819085a1e06d886bf21c |
completed | April 16, 2026, 5:37 p.m. |
Created at: April 16, 2026, 8:46 p.m.