This example demonstrates how to use an entity resolution system to extract and resolve entities from a legal document. The system leverages OpenAI's GPT-4 language model to achieve this task. The primary purpose of this example is to showcase the capabilities of the entity resolution system in a simple and illustrative manner.

Overview

The entity resolution system processes a given legal document and identifies key entities such as parties, dates, terms, and clauses. It then resolves relevant information to provide a structured output. This example uses a Python script to interact with the system and demonstrates the process with a sample legal contract.

How to Use

Input Document: Provide the legal document you want to analyze. The document should include relevant legal terms, dates, parties' names, and other pertinent information.
Entity Extraction: The system employs the GPT-4 model to extract entities from the input document.
Entity Resolution: Extracted entities are resolved to their absolute values when applicable. For instance, relative date phrases are converted to specific dates.
Dependency Handling: The system identifies dependencies between entities. If one entity's resolution depends on another's, it ensures proper order of resolution.

Limitations

The context window is the biggest limitation of the size of document, but I imagine a system where you stream chunks of the document into a model, that acculimates the entities in some state and formats a simple version back into the prompt (id, name, absolute_resolved_value) and the output emits only 'new' entities, thinking of it as a acculilating the object.