Named-entity recognition and classification (NERC) is an essential natural language processing (NLP) task involved in many applications like interactive question answering, summarising, relation extraction, and text mining. Available NERC corpora follow different annotation schemes that vary in terms of formats and levels of complexity according to research requirements: from 1-level hierarchy annotations (e.g., “Person”, “Location”, and “Organisation”) to multi-level schemes. Inspired by the work of the Universal Dependencies framework in terms of a standard representation of parsed trees, we developed the universal named-entity recognition (UNER) framework, which consists of a multi-level NERC hierarchy and a corresponding workflow that parses data from Wikipedia and DBpedia, translating it to UNER annotations. This chapter presents the UNER hierarchy and its workflow for data extraction and annotation. The proposed process was used to generate an English corpus, which was evaluated qualitatively and quantitatively. Furthermore, seven strategies for annotation improvement were presented and discussed, showing that the usage of information from the Open Event Knowledge Graph (OEKG) can improve our dataset.