I have loaded DBpedia, an RDF version of Wikipedia, into Dgraph, specifically the 2016-10 dump. In contrast to Wikipedia, DBpedia is less texty (at most there is a long abstract but not the entire article iirc).
The goal was to get a large graph (500m triples) with a wide long-tail schema (230k predicates) and to query those data with simple single-step path queries, so like a benchmark dataset. Benchmarking is meant not to measure how fast it is but how performance degrades with scale (constant, linearly, polynomial). Loading that data was very painful with 20.03.3 (memory-wise), but discussion with core devs gave me the impression the next version is much more stable and performant.
Query performace in my use-case is satisfying, except for some issues around pagination.
I think your problem statement needs a why, as in why would a graph database be beneficial, what is the use case / access pattern that Dgraph can improve.