Change Pandas column based on values in two other columns

I've tried a number of different solutions to this but can't seem to come across an answer which is functioning as necessary.

So basically I have a pandas dataframe as follows:

component   ...   rdfs:range
-----------------------------
class             NaN
property          xsd:int
instance          NaN
property          obj:ObjectName
property          xsd:string
property          obj:Object2Name
instance          NaN
class             NaN

What I need as an output is the following:

component ... subcomponent ... rdfs:range
-----------------------------------------------
class         NaN              NaN
property      data property    xsd:int
instance      NaN              NaN
property      object property  obj:ObjectName
property      data property    xsd:string
property      object property  obj:Object2Name
instance      NaN              NaN
class         NaN              NaN

I have a dictionary where I have all of the rdfs:range values that are data properties are enumerated, but the number of object properties is too large to catalogue manually (as well both the data properties and object properties have different prefixes, unlike above, so string matching is out of the question).

Ideal behavior is:

1. Keep "ontology subcomponent" as NaN for classes and instances.
2. Turn "ontology subcomponent" to whatever is in the dictionary based on the rdfs:range column.
3. Turn "ontology subcomponent" to "object property" for all properties not in the dictionary in step 2.

I have figured out how to achieve steps 1 and 2 separately, but they keep overwriting one another, i.e. sample_df["ontology subcomponent"] = sample_df["rdfs:range"].map(sample_dict) solves step 2 but any solution to steps 1 and 3 that I've found overwrites this solution or doesn't retain NaN in the correct spot for classes and instances.

Any help or pointing in the right direction would be extremely helpful!

I would just fillna all the subcomponents with "object property" right after the mapping then mask those with a component isin "class" or "instance" :

sub_comp = (
    sample_df["rdfs:range"].map(sample_dict)
        .fillna("object property") # <- 1st chain added
        .mask(sample_df["component"].isin(["class", "instance"])) # <- 2nd chain
)

sample_df.insert(1, "subcomponent", sub_comp)

Output :

print(sample_df)

  component     subcomponent       rdfs:range
0     class              NaN              NaN
1  property    data property          xsd:int
2  instance              NaN              NaN
3  property  object property   obj:ObjectName
4  property    data property       xsd:string
5  property  object property  obj:Object2Name
6  instance              NaN              NaN
7     class              NaN              NaN