Privacy policies play a critical role in disclosing the collection and sharing of personal information. However, due to their complex and lengthy nature, users often find it difficult to comprehend and therefore tend to simply ignore them. This paper presents a novel approach that leverages the capabilities of large language models to aid users with automatic tools that enable them to discern the policies and assess the privacy risks while minimizing the reliance on human-labeled data. In particular, our approach automatically maps each paragraph of a privacy policy to predefined categories and extract the privacy attributes and their relationships with the first party collection and third party sharing. These attributes and relationships are represented as a graph, enabling the use of readily available graph databases such as Neo4j. This lends itself to answer privacy questions as Cypher queries and to identify inconsistent privacy statements.