Stanford NER - Increase probability for a certain class
Your data set will influence the labeling results. If it is focused on organizations, the NER should favor them simply by virtue of the data it's fed. So you might need to do anything. But if you do observe undesirable behavior in the resulting NER, you can adjust the weights.

It's the same with any machine learning algorithm. A humorous demonstration was provided by Google's Deep Dream: it kept seeing dogs every where. Why? Because the data set they used for training had an abundance of dogs.

(And Carl Zeis should be labeled as a person. The company is Carl Zeiss.)