Machine learning can still make harmful mistakes. A solution would be tacit knowledge. Machine learning needs this type of knowledge to improve. An example of such knowledge that can help make the system draw better logical conclusions would be: if presented with an open fridge, then it could deduct that the food will go bad. Tacit knowledge or common-sense knowledge refers to the type of knowledge which is acquired through experience, the kind only humans can create. GWAPs (game with a purpose) have shown quite promising results for acquiring such knowledge. Unfortunately, it could still contain errors due to users who only want to harm the game data, etc. and there is no method for validating such knowledge without involving humans somehow. Therefore, from the previously stated problem, our goal has emerged - develop a method for validating an existing data set and for later training machine learning models using a GWAP. There has been work done before using GWAPs to elicit such information, yet they are limited in the sense that their main focus is set on data collection, not validation. Since very few projects looked into it, we decided to investigate a new GWAP which has as main purpose tacit knowledge validation. The main question which we aim to answer is "How can we elicit and validate tacit knowledge using a game with the following settings: single-player, textual concepts, goal: associate words with their concepts." The game presents hints to the users and they have to guess, as fast as possible and with the least amount of tries as possible, which answer is correct from the 6 options that are provided. The evaluation of the game will be made using standard metrics such as games played, time spent playing, number of users, etc. The conclusion is that the GWAP, even with the lack of data, was quite capable of analyzing the quality of the data set and reached a conclusion that is easily confirmed by a mere look over the initial data set.