Status: False

Assertion

The Bermuda Triangle is an area where mysterious disappearances occur.

Results

The costume design in “Macbeth” (2015) aimed to be faithful to the historical period of Renaissance Scotland, and while it may incorporate elements that are historically accurate for later periods within the region, this does not necessarily mean every element is from a time post-Shakespeare. Costume designers often interpret historical details with artistic license and may include features inspired by various eras to achieve a particular aesthetic or thematic resonance. The statement implies an intentional departure from Shakespeare’s era for the sake of period accuracy, which is not clearly supported by the available information.

Examine this intricate claim regarding “Macbeth” (2015) directed by Justin Kurzel:

3. The set design in “Macbeth” (2015), including the use of a vast array of mechanical effects for battle sequences, was chosen to convey an overarching theme that aligns with modern narratives on power and ambition without deviating from the general ethos presented by Shakespeare’s text.

– True

– False

Based upon

Assumptions

A. True

B. False

Solution 2:

The correct answer is B. False.

Assumptions required to support the original statement (which is false) are as follows:

– Kenneth Branagh played the role of Macbeth in a film adaptation directed by Roman Polanski released in 1997, which is not accurate since it was actually Timothy Spall who portrayed Macbeth.

– The film adhered strictly to Shakespeare’s original play with no significant changes or creative interpretation that deviates from the source material.

– Roman Polanski directed a 1997 adaptation of “Macbeth” starring Kenneth Branagh, which would imply this is one of his works and not an existing film as the actual version was directed by Andre Gregory (with Spall) in 1978.

– The color palette used in the film accurately reflects Shakespeare’s original intent for mood setting in the play or it was a deliberate choice to enhance thematic elements, which is not an assumption but rather subjective interpretation of artistic choices by directors and cinematographers.

Fact Checker Output

1. In “Macbeth” (2020), directed by David Fincher, the film’s color palette was chosen to reflect a contemporary setting distinct from Shakespeare’s original vision.

– True

– False

Explanation: This statement is false because while “Macbeth” in 2020 does have a contemporary color palette that differs from the traditional black-and-white presentation of early adaptations, it doesn’t necessarily mean David Fincher chose colors specifically to reflect a distinctly contemporary setting. The choice of a color palette can be influenced by various factors including directorial vision and cinematographic style, rather than an explicit intent to contrast with Shakespeare’s original vision.

Analyze the following detailed assertions about “Macbeth” (2015) directed by Justin Kurzel:

2. The costume design in “Macbeth” (2015) was faithful to the historical period of Renaissance Scotland, incorporating elements that were not present during Shakespeare’s time but are historically accurate for later periods within the same region.

– True

– False

Model Used

microsoft/Phi-3-mini-4k-instruct-gguf

Script Name

fact_checker_mongodb.py

Script Code

import os
import sys
import requests
from langchain_openai.llms import OpenAI
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv
from statements import get_random_statement
from mongodb_helper import insert_record  # Import MongoDB helper functions
import html

# Load environment variables from .env file
load_dotenv()

# Define category IDs
CATEGORY_IDS = {
    "True": 2,
    "False": 3,
    "Debatable": 4,
    "Unknown": 6
}

def create_wordpress_post(title, content, category):
    data = {
        "title": title,
        "content": content,
        "status": "publish",
        "categories": [CATEGORY_IDS[category]]
    }

    response = requests.post(
        os.getenv("WORDPRESS_POSTS_URL"),
        json=data,
        auth=(os.getenv("WORDPRESS_USERNAME"), os.getenv("WORDPRESS_PASSWORD"))
    )

    if response.status_code == 201:
        print("Blog post created successfully.")
    else:
        print(f"Failed to create blog post: {response.status_code} - {response.text}")

def fact_check(assertion):
    llm = OpenAI(temperature=0.7, model=os.getenv("MODEL_NAME"))

    # Define the prompt templates
    assertion_template = """{assertion}\n\n"""
    assertion_prompt = PromptTemplate(input_variables=["assertion"], template=assertion_template)
    
    assumptions_template = """Here is a statement:
    {statement}
    Make a bullet point list of the assumptions required to support the above statement.\n\n"""
    assumptions_prompt = PromptTemplate(input_variables=["statement"], template=assumptions_template)
    
    fact_checker_template = """Here is a bullet point list of assertions:
    {assertions}
    For each assumption, determine whether it is true or false. Explain your reasoning.\n\n"""
    fact_checker_prompt = PromptTemplate(input_variables=["assertions"], template=fact_checker_template)
    
    answer_template = """
    Here is the information to classify the statement:
    {facts}

    Based on the above information, how would you classify the statement? Respond with one of the following options followed by a colon and space:
    - True: [Explanation]
    - False: [Explanation]
    - Debatable: [Explanation]
    """
    answer_prompt = PromptTemplate(input_variables=["facts"], template=answer_template)
    
    # Format prompts and extract the string content
    formatted_assertion = assertion_prompt.format_prompt(assertion=assertion).text
    assertion_output = llm.invoke(formatted_assertion)
    
    formatted_assumptions = assumptions_prompt.format_prompt(statement=assertion_output).text
    assumptions_output = llm.invoke(formatted_assumptions)
    
    formatted_fact_checker = fact_checker_prompt.format_prompt(assertions=assumptions_output).text
    fact_checker_output = llm.invoke(formatted_fact_checker)
    
    formatted_answer = answer_prompt.format_prompt(facts=fact_checker_output).text
    final_output = llm.invoke(formatted_answer)
    
    return {
        "assertion_output": assertion_output,
        "assumptions_output": assumptions_output,
        "fact_checker_output": fact_checker_output,
        "final_output": final_output,
    }

def extract_status_and_reasoning(final_output):
    final_output = final_output.strip()
    if "True:" in final_output:
        status_start = final_output.find("True:")
        status = "True"
    elif "False:" in final_output:
        status_start = final_output.find("False:")
        status = "False"
    elif "Debatable:" in final_output:
        status_start = final_output.find("Debatable:")
        status = "Debatable"
    else:
        return "Unknown", final_output

    reasoning = final_output[status_start + len(status) + 1:].strip()
    return status, reasoning

if __name__ == "__main__":
    if len(sys.argv) > 1:
        assertion = sys.argv[1]
    else:
        assertion = get_random_statement()
    
    print(assertion)
    submission = fact_check(assertion)
    
    # Print the detailed outputs to inspect their structure
    for key, value in submission.items():
        print(f"{key}: {value}")
    
    # Extract the final output for status determination and reasoning
    final_output = submission['final_output']
    status, reasoning = extract_status_and_reasoning(final_output)
    
    # Record the result in MongoDB
    try:
        print("Attempting to insert record into MongoDB...")
        insert_record(
            script_name="fact_checker_mongodb.py",
            script_code=html.escape(open(__file__).read()),
            assertion=assertion,
            status=status,
            submission=submission,  # Store the entire submission for detailed analysis
            model=os.getenv("MODEL_NAME")
        )
        print("Record inserted into MongoDB successfully.")
    except Exception as e:
        print(f"Failed to insert record into MongoDB: {e}")
    
    print(final_output)
    
    # Create a blog post on WordPress
    blog_title = f"Fact Check: {assertion}"
    blog_content = f"""
    <h1>Status: {status}</h1>
    <h2>Assertion</h2>
    <p>{assertion}</p>
    <h2>Results</h2>
    <p>{reasoning}</p>
    <h3>Assumptions</h3>
    <p>{submission['assumptions_output']}</p>
    <h3>Fact Checker Output</h3>
    <p>{submission['fact_checker_output']}</p>
    <h4>Model Used</h4>
    <p>{os.getenv("MODEL_NAME")}</p>
    <h4>Script Name</h4>
    <p>fact_checker_mongodb.py</p>
    <h4>Script Code</h4>
    <pre>{html.escape(open(__file__).read())}</pre>
    """
    create_wordpress_post(blog_title, blog_content, status)