**The Three Terms Task (3TT) - an open benchmark to compare human and artificial semantic representations.** Word processing entails retrieval of a unitary yet multidimensional semantic representation (e.g., a lemon's colour, flavour, size, possible use) and has been the object of investigation for both cognitive neuroscience and artificial intelligence. To enable the direct comparison of human and artificial semantic representations and to support the use of natural language processing (NLP) as computational models of human understanding, a critical challenge is the development of benchmarks of appropriate size and complexity. Here we present a dataset probing semantic knowledge with a three-terms semantic associative task: which of two target words is more closely associated with a given anchor (e.g., is lemon closer to squeezer or sour?). The dataset includes both abstract and concrete nouns for a total of 10,107 triplets. For the 2,255 triplets with the highest or lowest agreement among NLP word embeddings, we additionally collected ground-truth behavioural similarity judgments from 1,322 human raters. We hope that this openly available, large-scale dataset will be a useful benchmark for both computational and neuroscientific investigations of semantic knowledge. It should be noted that the two tables, containing raw anonymized individual subject data (i.e., participant demographics, Results_Demographics_1322.csv, and individual's ratings for each stimuli, Results_Responses_1322.csv) will be accessible only after proper registration with the CNeuromod databank (https://docs.cneuromod.ca/en/2020-alpha2/ACCESS.html) due to ethical considerations.