Entity resolution (ER) is an important aspect of data integration and data cleaning, and is also a necessary pre-process step of big data analytics and mining. Traditional batch based ER's overall runtime is costly, and cannot satisfy current (nearly) real-time data applications' requirements. Therefore, time constraint entity resolution (TC-ER) is focused on, while core problem is record pair ranking according to match probability both information inner blocks and information across blocks are analyzed from multi-pass blocking respectively, and two basic recordsmatch probability methods are proposed. The basic methods are improved by proposing an advanced record match probability method based on similarity flowing over a biparitite graph.A bipartite graph is constructed according to record pairs, blocks, and relations between them. Similarities iteratively flow between pair nodes and block nodes over the bipartite graph until convergence. The convergence result is computed with fixpoint iterations. An approximate convergence computation mehod is proposed to reduce cost, and it improves real-time recall in TC-ER. Finally, the proposed methods are evaluated on two datasets, which shows their effectiveness and also tests different aspects of the proposed methods.