We here describe the creation of manually annotated training data for the Kaggle task “What do we know about COVID-19 risk factors?”. We applied our text mining tool on the “COVID-19 Open Research Dataset” to i) select data for manual annotation, ii) classify the data into initially established classification categories, and iii) analyse our data set in search for potential refinements of the annotation categories. The process resulted in a corpus consisting of 50,000 tokens, for which each token is annotated as to whether it is part of an expression that functions as a “risk factor trigger”. Two types of risk factor triggers were annotated, those indicating that the text describes a risk factor, and those indicating that something could not be shown to be a risk factor.