Natural Language Inference (NLI) is a natural language processing task that seeks to identify whether one sentence entails another. The traditional approach to NLI has been to train a model on a large corpora of data. However, these models are often black-boxes and offer no explanation about their decisions. In this work, we apply model-theoretics, a framework from adopted from formal logic and linguistics, to solving NLI tasks. To simulate the model-theoretic hypothesis of entailment, we use a language model to generate natural language contexts for a pair of sentences and then define a new classification method to evaluates these contexts and determine entailment relations. Because this approach applies a logical framework to language, it provides much more interpretability than traditional NLI methods. This work-in-progress paper applies this method to preexisting NLI datasets and demonstrates that our method shows promise in achieving high-levels of accuracy without requiring model training or the availability of a large corpora of training data.