Strategies for handling unlabeled data which is slightly different from the labeled data
There is no straightforward way to deal with this scenario, but here are some ideas:


Augment the A/B/C label set with an extra label "U" for "unknown". Now all samples are labeled.
Predict A/B/C labels from the unlabeled samples from the other pieces of data in the sample. Now all samples are labeled.
Train a system for data with labels and another system for data without them. Use them according to the availability of the label. It would be great if your ML approach allows for sharing parts of the model for labeled data and the one for unlabeled data (e.g. neural networks).