  Matching mentions of persons to the actual persons (the name disambiguation
problem) is central for several digital library applications. Scientists have
been working on algorithms to create this matching for decades without finding
a universal solution. One problem is that test collections for this problem are
often small and specific to a certain collection. In this work, we present an
approach that can create large test collections from historical metadata with
minimal extra cost. We apply this approach to the DBLP collection to generate
two freely available test collections. One collection focuses on the properties
of defects and one on the evaluation of disambiguation algorithms.
