Does it make sense to label a dataset manually?
The chicken egg dilemma

What came first: the labeled data or the machine learning model?


If you have labeled data, then you can train a machine learning model.
If you have a trained machine learning model, then you can label data.


Precision vs. Recall

Suppose you are in case 2 (you have a model). Then how is the precision and recall of your model? Unless both are $100\%$, you might still want to label data.

                       recall
                 100%     |     &lt; 100%
          --------------------------------------
          Your model is   | You can confidently
p   100%  perfect. You    | trust the labels you
r         don't need more | get. But your model 
e         data.           | might miss some.
c         --------------------------------------
i         You get         | You get some 
s  &lt; 100% labels, but you | labels, but you
i         still need to   | still need to
o         confirm that    | confirm that
n         they are correct| they are correct


Most people will be at the lower-right quadrant (precision and recall will be $\lt100\%$). The goal will be to get as close as possible to $100\%$. Therefore, the model will output labels which can be the basis for manual labeling.

Your case

If you can label all rows with some rules and you are creating these rules, then, in fact, you are already manually labeling data. Further, you could use a decision tree later on, to model all your rules in order to deal with new data points that come your way.