Humans or intelligent software agents are increasingly faced with the challenge of making decisions based on large volumes of streaming information from diverse sources. Decision makers must process the observed information by inferring additional information, estimating its reliability and orienting it for decision-making. In this paper, we propose a stream-reasoning framework that achieves all these goals. While information is streamed as unstructured reports (e.g., text in natural language) from unreliable sources, our framework first converts it into a structured form using Controlled English and then it derives some facts that are useful for decision-making, and estimates the trust in these facts. Lastly, various facts are fused based on their trustworthiness. This process is totally undertaken on streaming information resulting in new facts being inferred from incoming information which immediately goes through trust assessment framework and trust is propagated to the inferred fact. In this paper, we propose a comprehensive framework where unstructured reports are streamed from heterogeneous and potentially untrustworthy information sources. These reports are processed to extract valuable uncertain information, which is represented using controlled natural language and subjective logic. Additional information is inferred using deduction and abduction operations over subjective opinions derived from the reports. Before fusing extracted and inferred opinions, the framework estimates trustworthiness of these opinions, detects conflicts between them, and resolve these conflicts by analysing evidence about the reliability of their sources. Lastly, we describe an implementation of the framework using International Technology Alliance (ITA) assets (Information Fabric Services and Controlled English Fact Store) and present an experimental evaluation that quantifies the efficiency with respect to accuracy and overhead of the proposed framework.