This nodes calculates some quality measures like precision, recall and f1-measures and counts the amount of true positives, false negatives and false positives to validate a Stanford NLP NE model. Internally the node tags the incoming test document set with a dictionary tagger which is based on the dictionary which was used for tagging the training set in the learner node. After tagging the documents, the input model tags the documents again and the node calculates the differences between the tags created by the dictionary tagger and the tags created by the input model.