Competency Questions (CQs) are pivotal in knowledge engineering, guiding the design, validation, and testing of ontologies. A number of diverse formulation approaches have been proposed in the literature, ranging from completely manual to those that use Large Language Model (LLM). However, attempts to characterise the outputs of these approaches and their systematic comparison are scarce. This paper presents an empirical comparative evaluation of three distinct CQ formulation approaches: manual formulation by ontology engineers, instantiation of CQ patterns, and generation using state of the art LLMs. CQs are generated using each approach given a set of requirements for cultural heritage, and they are assessed empirically across different dimensions: degree of acceptability, ambiguity, relevance, readability and complexity. Our contribution is twofold: (i) the first multi-annotator dataset of CQs generated from the same source using different methods; and (ii) a systematic comparison of the characteristics of the CQs resulting from each approach. Our study shows that different CQ generation approaches have different characteristics and that LLMs can be used as a way to initially elicit CQs, however these are sensitive to the model used to generate CQs and they generally require a further refinement step.