[good first issue - advanced][Example] Create a dataflow modeling information extraction using an LLM

Write an example dataflow that uses Hamilton to model an information extraction task using an LLM.

For example:
1. given an output schema
2. given input text
3. make a prompt that is sent to an LLM API (pick one)
4. then write a function to validate the output

For inspiration you can look at [Langchain's implementation](https://github.com/langchain-ai/langchain/blob/490ad93b3cf7d24b30f8993f860b654ff107e638/libs/langchain/langchain/chains/openai_functions/extraction.py#L46).

The code for this example should end up under the /examples/LLM_Workflows directory.

Start by reviewing the existing examples in /examples/LLM_Workflows and the linked LangChain extraction implementation. Build an example that defines an output schema and input text, sends a generated prompt to one LLM API, and validates the response. Done means the complete dataflow example is placed under /examples/LLM_Workflows.

Written by the indexing model from the issue text.