diff --git a/docs/source/usage/csg.md b/docs/source/usage/csg.md
index 7d51087..f3b9297 100644
--- a/docs/source/usage/csg.md
+++ b/docs/source/usage/csg.md
@@ -170,7 +170,7 @@ lower-priority timeseries was used to fill the holes.
 The {py:func}`primap2.csg.compose` function creates a composite time series according to
 the given priorities and strategies, but it does not take care of pre- and postprocessing
 of the data. It will carry along unnecessary data and the resulting dataset will miss the
-priority coordinates. The {py:func}`primap2.csg.create_composite_source` function takes acre
+priority coordinates. The {py:func}`primap2.csg.create_composite_source` function takes care
 of these steps and prepares the input data and completes the output data to a primap2 dataset
 with all desired dimensions and metadata.
 
@@ -178,7 +178,7 @@ The function takes the same inputs as {py:func}`primap2.csg.compose` with additi
 define pre- and postprocessing:
 
 * **result_prio_coords** Defines the vales for the priority coordinates in the output dataset. As the
-priority coordinates differ for all input sources there is no canonical vale
+priority coordinates differ for all input sources there is no canonical value
 for the result and it has to be explicitly defined
 * **metadata** Set metadata values such as title and references
 
diff --git a/primap2/csg/_wrapper.py b/primap2/csg/_wrapper.py
index da6d4ce..a62aee3 100644
--- a/primap2/csg/_wrapper.py
+++ b/primap2/csg/_wrapper.py
@@ -18,10 +18,7 @@ def set_priority_coords(
 
     """
     for dim in dims.keys():
-        if "terminology" in dims[dim].keys():
-            terminology = dims[dim]["terminology"]
-        else:
-            terminology = None
+        terminology = dims[dim].get("terminology", None)
         ds = ds.pr.expand_dims(dim=dim, coord_value=dims[dim]["value"], terminology=terminology)
 
     return ds