Triple
T11002229
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gibbs sampling |
E260029
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Markov chain Monte Carlo algorithm |
C6819
|
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: Markov chain Monte Carlo algorithm Context triple: [Gibbs sampling, instanceOf, Markov chain Monte Carlo algorithm]
-
A.
algorithm
chosen
An algorithm is a finite, well-defined sequence of computational steps or rules designed to solve a specific problem or perform a particular task.
-
B.
simulation technique
A simulation technique is a systematic method for modeling and imitating the behavior of real or hypothetical systems over time to analyze their performance, predict outcomes, or support decision-making.
-
C.
stationary iterative method
A stationary iterative method is a numerical algorithm for solving linear systems that repeatedly updates an approximate solution using a fixed iteration matrix and rule that do not change between iterations.
-
D.
statistical inference method
A statistical inference method is a systematic procedure for drawing conclusions about a population’s properties based on observed sample data, often quantifying uncertainty through probabilities or confidence measures.
-
E.
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.
- 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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
Created at: April 8, 2026, 9:25 p.m.