Triple
T12061444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Åström–Wittenmark adaptive control framework |
E287179
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | model reference adaptive control framework |
C22748
|
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: model reference adaptive control framework Context triple: [Åström–Wittenmark adaptive control framework, instanceOf, model reference adaptive control framework]
-
A.
export control law
Export control law is the body of legal rules and regulations that govern the transfer of goods, technology, software, and services across national borders to protect national security, foreign policy interests, and international obligations.
-
B.
control engineering concept
chosen
A control engineering concept is a theoretical or practical principle used to analyze, design, and optimize systems that automatically regulate their behavior to achieve desired performance.
-
C.
model-based reinforcement learning algorithm
A model-based reinforcement learning algorithm is a decision-making method that learns or uses an explicit model of the environment’s dynamics to plan and select actions that maximize long-term rewards.
-
D.
robot control system
A robot control system is a coordinated set of hardware and software components that interpret sensor data, execute decision-making algorithms, and generate actuator commands to direct a robot’s behavior in real time.
-
E.
adaptive learning rate method
An adaptive learning rate method is an optimization technique that automatically adjusts the step size for each parameter during training based on past gradient information to improve convergence speed and stability.
- 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_69d6ab4846e081908ee7bbd66a6d3459 |
completed | April 8, 2026, 7:23 p.m. |
Created at: April 8, 2026, 9:48 p.m.