This paper presents project and experiment results involving named entity recognition (NER) in entity disambiguation. With the use of entity-fishing, a tool to automate the named-entity recognition and disambiguation, the goal of this project is to see the impact of the NER involvement in disambiguation task. By exploiting Wikidata which principally digging into more than 50 million entities and more than 600 million statements in form of property-value pairs, our first interest is to find the patterns for determining the type of each entity based on its characteristics. The result in a form of a mapping system containing entities and their proper types (e.g., Person, Location) is then carried further into entity-fishing to see the evaluation results before and after involving NER in the ranker and selector models. For comparison, in addition of using NER generated from the Wikidata statements exploitation, this project uses as well NER generated from the Grobid-NER project.