Triple
T25602691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cover’s theorem |
E641828
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | result in statistical pattern recognition |
C8028
|
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: result in statistical pattern recognition Context triple: [Cover’s theorem, instanceOf, result in statistical pattern recognition]
-
A.
statistical classification
Statistical classification is the process of assigning items or observations to predefined categories or classes based on their measured features using probabilistic or algorithmic decision rules.
-
B.
result in probability theory
chosen
In probability theory, a result is a formally stated and proven fact—such as a theorem, lemma, or corollary—that describes a property or relationship involving probabilistic concepts like random variables, events, or distributions.
-
C.
statistical classification system
A statistical classification system is a structured framework that organizes data, entities, or phenomena into predefined categories based on quantitative criteria and statistical methods to enable consistent analysis and comparison.
-
D.
machine learning paradigm
A machine learning paradigm is a conceptual framework that defines how models learn from data, including the assumptions, learning objectives, and training procedures that guide the development and application of algorithms.
-
E.
recognition framework
A recognition framework is a structured approach or system for identifying, categorizing, and validating entities, patterns, or achievements according to defined criteria and processes.
- 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_69e75dc6ccf081908d49578fd36a76d5 |
completed | April 21, 2026, 11:21 a.m. |
Created at: April 21, 2026, 4:36 p.m.