nested_cv resampling using: Outer 10-fold cross validation, Inner validation_split for development

I want to tune a ridge regression, where I for the outer sampling method use 10-fold cross validation and for the inner sampling method use one part for training and another part for development. Hence I want to use a training, development and testing framework.
My question are:

  1. Is it in this scenario correct to use validation_split for the inner sampling in nested_cv (see example code below)?
  2. Also, is there a good way to confirm that this methodological flow is actually happening?

For example, is a confirmation that when using validation_split for the inner split, it says "Validation" (which equal to the Development portion that I'm after?):


Whereas, when using, e.g., n-fold for the inner sampling as well, it instead says "Assessment":


That is, validation means that the data is not used for training; whereas Assessment indicates that the data has or will be used for training (in n-fold cross-validation).

Example data to check splits.

x1 <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
  y1 <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
  x1y1 <- tibble(x1, y1)
  nested_resampling_dev <- rsample::nested_cv(x1y1,
                                              outside = rsample::vfold_cv(v = 10, repeats = 1),
                                              inside  = rsample::validation_split(prop = 3/4))



Comparison data

nested_resampling_2_nfolds <- rsample::nested_cv(x1y1,
                                                   outside = rsample::vfold_cv(v = 10, repeats = 1),
                                                   inside  = rsample::vfold_cv(v = 10, repeats = 1))


(I have had much help developing it from this nested resampling tutorial:

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