In the initial version of bbw we used the OpenRefine Reconciliation API (https://wikidata.reconci.link/en/api). However, due to a very noisy dataset in the SemTab2020 competition the OpenRefine Reconciliation API was simply unable to handle with those very difficult mistakes in the words and it returned often no results at all. We decided to disable a lookup over the OpenRefine Reconciliation API. Instead of it we used a meta-lookup over many search and metasearch engines. This allowed us to resolve even tricky spelling mistakes. OpenRefine is a great software, but if I would have noisy data, I would use bbw. By the way, I do not know how these two would compare at a clean dataset, it has to be tested. I can only add that bbw achieved F1-scores above 0.99 for properties and roughly 0.98 for types and entities even at a very noisy dataset.
