Embodied theories of language emphasize the role of sensorimotor experience in linguistic knowledge. Central to testing these theories is the creation of large datasets of linguistic norms, which contain judgments about a word's sensorimotor associations and can be used to predict human behavioral or brain data - sometimes in contrast to competing variables, such as those derived from distributional language models. Yet many of these datasets contain judgments about words in isolation, despite the fact that most words are ambiguous, making it difficult to determine which meaning of a word is characterized by its rating (e.g., "wooden table" vs. "data table"). In the current work, we introduce a new lexical resource (directly inspired by the Lancaster sensorimotor for 112 English words, each rated in four different contexts (448 sentences total). We demonstrate: first, that these ratings encode overlapping but distinct information from the Lancaster sensorimotor norms; second, that decontextualized ratings likely reflect the more dominant meaning of ambiguous words; third, that homonyms have more distinct sensorimotor profiles than polysemes; fourth, that the contextualized sensorimotor distance between two uses of an ambiguous word predicts human judgments about semantic relatedness; and fifth, that ratings derived from GPT-4 align reasonably well with human judgments. We conclude by suggesting that contextualized ratings like these can be used both to inform competing theories of semantic representations and also to evaluate or "probe" the ability of LMs to recover sensorimotor information.