Cherre’s knowledge graph is a model of the entire US real estate ecosystem. The graph incorporates hundreds of millions of entities such as properties, addresses, individual and commercial owners, lenders, brokers, estate managers, lawyers etc. as nodes – while the edges are various types of connections between the entities. A wealth of attributes are associated with each entity. Cherre’s knowledge graph is a closed-world graph: it allows inferring an absence of connection between two entities if there is no edge between them in the graph. Furthermore, Cherre’s graph is temporal: edges and nodes are being added and deleted on a timely basis. Some of the main challenges in constructing a closed-world graph from noisy data sources are entity resolution and disambiguation. In this talk, we will present parallel algorithms for entity resolution and disambiguation in Cherre’s knowledge graph, and outline our current work on assessing entity similarities using (temporal) node embedding.