Natural Language Processing (NLP) have researched and formulated computational mechanisms to ease Human-Computer Interaction (HCI). From the conceptual point of view, a NLP system can be divided into three main processes: morphology, syntax and semantics. Every process has to deal with multiple interpretations for the same word or phrase; as a result, ambiguity is originated. To solve ambiguity, statistics-based, artificial-intelligence-based, and hybrid methods have been proposed; however, there are still difficulties to be solved, for example wasting of lexical and computational resources and using of restricted-domain elements. Here in this paper we propose a method for solving coordinative and prepositional syntactic ambiguity; this method includes the definition of a set of heuristic rules. Also, we show the implementation of the method using the python language in conjunction with the Natural Language Tool Kit (NLTK), and we exemplify disambiguation of two case studies.