But in practice, the opposite seems to be the case, due to actual mentions being more heavily skewed towards popular entities than in WSD, a plethora of available training data in the form of human-entered anchor-texts of hyperlinks on the web, and higher inter-annotator agreement, which indicates more crisp differences between possible shades of meanings than in WSD.
both popularity-based dictionary lookups (MFS heuristics) and supervised classifiers, which are traditional WSD architectures, perform better for NED than for WSD.