1. A method comprising: searching, by a node of a system hosting an in-memory database, a set of candidate records to identify one or more candidates matching one or more extracted features, wherein an extracted feature that matches a candidate is a primary feature; associating, by the node, each of the extracted features with one or more machine-generated topic identifiers (“topic IDs”); disambiguating, by the node, each of the primary features from one another based on relatedness of topic IDs; identifying, by the node, a set of secondary features associated with each primary feature based upon the relatedness of topic IDs; disambiguating, by the node, each of the primary features from each of the secondary features in the associated set of secondary features based on relatedness of topic IDs; linking, by the node, each primary feature to the associated set of secondary features to form a new cluster; determining, by the node, whether the new cluster matches an existing knowledgebase cluster, wherein, when there is a match, determining, by the disambiguation module of the in-memory database server computer, an existing unique identifier (“unique ID”) corresponding to each matching primary feature in the knowledgebase cluster and updating the knowledgebase cluster to include the new cluster; and when there is no match, creating, by the node, a new knowledgebase cluster and assigning a new unique ID to the primary feature of the new knowledgebase cluster; and transmitting, by the node, one of the existing unique ID and the new unique ID for the primary feature.