[docs] fix a couple of non-code "builtin" -> "built-in"

docs/source/getting_started.md

@@ -99,7 +99,7 @@ parameter values which maximises reduction error.
 
 ## The thermalblock demo explained
 
-In the following we will walk through the thermal block demo step by
+In the following we will go through the thermal block demo step by
 step in an interactive Python shell. We assume that you are familiar
 with the reduced basis method and that you know the basics of
 [Python](<https://www.python.org>) programming as well as working

docs/source/tutorial_basis_generation.md

@@ -106,7 +106,7 @@ from pymor.basic import *
 ```
 
 Then we build a 3-by-3 thermalblock problem that we discretize using pyMOR's
-{mod}`builtin discretizers <pymor.discretizers.builtin>` (see
+{mod}`built-in discretizers <pymor.discretizers.builtin>` (see
 {doc}`tutorial_builtin_discretizer` for an introduction to pyMOR's discretization toolkit).
 
 ```{code-cell}
@@ -187,7 +187,7 @@ fom.solution_space
 
 which means that the created {{ VectorArrays }} will internally hold
 {{ NumPy_arrays }} for data storage. The number is the dimension of the
-vector. We have here a {{ NumpyVectorSpace }} because pyMOR's builtin
+vector. We have here a {{ NumpyVectorSpace }} because pyMOR's built-in
 discretizations are built around the NumPy/SciPy stack. If `fom` would
 represent a {{ Model }} living in an external PDE solver, we would have
 a different type of {{ VectorSpace }} which, for instance, might hold a

docs/source/tutorial_mor_with_anns.md

@@ -107,7 +107,7 @@ Dirichlet boundary conditions
 u((x_1, x_2), \mu) = 2x_1\mu + 0.5,\quad x=(x_1, x_2) \in \partial\Omega.
 ```
 
-We discretize the problem using pyMOR's builtin discretization toolkit as
+We discretize the problem using pyMOR's built-in discretization toolkit as
 explained in {doc}`tutorial_builtin_discretizer`:
 
 ```{code-cell}