Semantic processing systems like this (it's not something new) aren't usually able to determine correctness. The truth of a statement is assumed and the best these
NLP engines can do at the moment is identify conflicts and maybe use some reputation metrics to assign a veracity rating to a particular statement, or notify the user that there are differing conclusions. These systems are just really, like the summary states, "
information extraction" systems. Just as a regular search engine will return you the results from the data set, that's what these types of semantic extraction engines usually do, except the data is processed in a semantically-organized way so that you can query with semantics/natural language constraints instead of just keywords and boolean constraints.
There are some that incorporate some intention or opinion polarity detection, but even those are not capable to sorting "truth" versus "conspiracy".
Additionally, semantic extraction output, like
named entities and
semantic relations, are useful for many other applications.