Despite the remarkable achievements of Large Language Models (LLMs) in various Natural Language Processing tasks, their competence in abstract language understanding remains a relatively under-explored territory. Figurative language interpretation serves as ideal testbed for assessing this as it requires models to navigate beyond the literal meaning and delve into underlying semantics of the figurative expressions. In this paper, we seek to examine the performance of GPT-3.5 in zero-shot setting through word-level metaphor detection. Specifically, we frame the task as annotation of word-level metaphors in proverbs. To this end, we employ a dataset of English proverbs and evaluated its performance by applying different prompting strategies. Our results show that the model shows a satisfactory performance at identifying word-level metaphors, particularly when it is prompted with a hypothetical context preceding the proverb. This observation underscores the pivotal role of well-designed prompts for zero-shot settings through which these models can be leveraged as annotators for subjective NLP tasks.