Large Language Models (LLMs) exhibit inequalities with respect to various cultural contexts. Most prominent open-weights models are trained on Global North data and show prejudicial behavior towards other cultures. Moreover, there is a notable lack of resources to detect biases in non-English languages, especially from Latin America (Latam), a continent containing various cultures, even though they share a common cultural ground. We propose to leverage the content of Wikipedia, the structure of the Wikidata knowledge graph, and expert knowledge from social science in order to create a dataset of Questions/Answers (Q/As) pairs, based on the different popular and social cultures of various Latin American countries. We create a database of around 23k questions and associated answers extracted from 23k Wikipedia articles, and transformed into a multiple-choice questions (MCQ) in Spanish and Portuguese, in turn translated to English. We use this MCQ to quantify the degree of knowledge of various LLMs and find out \textit{(i)} a discrepancy in performances between the Latam countries, ones being easier than others for the majority of the models, \textit{(ii)} that the models perform better in their original language, \textit{(iii)} that Iberian Spanish culture is better known than Latam one. Our code, our results for reproducing the results, and all datasets by region will be available.