Entity resolution (ER) seeks to identify which records in a data set refer to the same real-world entity. Given the diversity of ways in which entities can be represented, ER is known to be a challenging task for automated strategies, but relatively easier for expert humans. Nonetheless, also humans can make mistakes. Our contribution is an error correction toolkit that can be leveraged by a variety of hybrid human-machine ER algorithms, based on a formal way for selecting "control queries" for

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... the human experts. We demonstrate empirically that less recent ER algorithms equipped with our tool can perform even better than most recent ER methods with built-in error correction.