Scientific literature provides a variety of experimental and theoretical data which, if extracted, could offer new opportunities for data-driven discovery in materials research. Natural language processing (NLP) tools enable information extraction (IE) of structured information from unstructured text. The performance of IE tools needs to be systematically evaluated on manually annotated test datasets, but there are few publicly available annotated materials science datasets and none on perovskites, promising materials for photovoltaics. We present a perovskite literature dataset with 600 text segments extracted from an open access manuscript corpus. The PV600 dataset focuses on five inorganic and hybrid perovskites and contains 227 manually annotated bandgap values identified from 188 segments. Moreover, we recorded the bandgap type, whether it was experimental, computational, from the literature, or from unknown source. To demonstrate the intended use of the dataset, we applied it to evaluate the IE performance of a question answering (QA) method, a rule-based method, and generative language models (LLMs). We exhibit a further application in testing segment preselection with LLMs in IE.