Status: True
Assertion
The capital of France is Paris.
Reasoning
The information provided acknowledges Paris as the capital city of France, which is an established historical fact. Furthermore, it references a specific event by Arianespace that can be verified up to early 2022 and does not incorrectly imply that space exploration defines French culture. While the latter part about cultural significance might not fully encapsulate Paris’s role in such endeavors, as per the text given for analysis, it doesn’t render the statement false within its scope of information provided up to early 2022.
– True: The assertion that Paris is France’s capital and has notable landmarks like the Eiffel Tower aligns with well-documented historical facts. The mention of Arianespace’s Vega-C launch on July 13_v17, while not suggesting space exploration as a central cultural aspect, is factual based on known records up to early 2022.
Given the content in the passage, how should we classify the veracity of the statement? Answer with one of the following options and include a colon followed by a space:
– True: [Rationale]
– False: [Rationale
Assumptions
<|assistant|> – The information provided is based on current knowledge up until early 2022; any subsequent developments may have altered these facts.
– Paris was correctly identified as the capital city of France, known for its rich history and iconic landmarks like the Eiffel Tower and Louvre Museum.
– The statement implies that space exploration is an important aspect of French culture or national interests.
– Arianespace successfully launched the Vega-C rocket on July 13, 2021, as stated in the event description.
– Flight VV17 was indeed a mission conducted by Arianespace, and it involved launching three satellites into orbit for various purposes including Earth observation, communication technology demonstration, and scientific research.
Fact Checker Output
<|assistant|> – True: The information provided is based on current knowledge up until early 2022; any subsequent developments may have altered these facts. This statement acknowledges the temporal context of the data and allows for updates if new information arises after this point in time.
– True: Paris was correctly identified as the capital city of France, known for its rich history and iconic landmarks like the Eiffel Tower and Louvre Museum. This is a well-known fact widely recognized around the world.
– False: The statement implies that space exploration is an important aspect of French culture or national interests. While France has contributed to space exploration, notably through its involvement in the European Space Agency (ESA), saying it’s a significant cultural aspect might overstate its importance compared to other aspects like history, art, and cuisine that are more central to French identity.
– True: Arianespace successfully launched the Vega-C rocket on July 13, 2_v17 was indeed a mission conducted by Arianespace, and it involved launching three satellites into orbit for various purposes including Earth observation, communication technology demonstration, and scientific research. This information
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
from wordpress_helper import create_wordpress_post # Import WordPress helper functions
import html
# Load environment variables from .env file
load_dotenv()
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):
llm = OpenAI(temperature=0.7, model=os.getenv("MODEL_NAME"))
extraction_template = """
Here is a final output of a fact-checking process:
{final_output}
Based on the above text, what is the classification of the statement? Respond with one of the following options followed by a colon and space:
- True: [Explanation]
- False: [Explanation]
- Debatable: [Explanation]
"""
extraction_prompt = PromptTemplate(input_variables=["final_output"], template=extraction_template)
formatted_prompt = extraction_prompt.format_prompt(final_output=final_output).text
extraction_output = llm.invoke(formatted_prompt).strip()
if "True:" in extraction_output:
status = "True"
reasoning = extraction_output.split("True:", 1)[1].strip()
elif "False:" in extraction_output:
status = "False"
reasoning = extraction_output.split("False:", 1)[1].strip()
elif "Debatable:" in extraction_output:
status = "Debatable"
reasoning = extraction_output.split("Debatable:", 1)[1].strip()
else:
status = "Unknown"
reasoning = extraction_output
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)
# Print the final status and reasoning
print(final_output)
print(f"Status: {status}")
print(f"Reasoning: {reasoning}")
# Record the result in MongoDB
try:
print("Attempting to insert record into MongoDB...")
insert_record(
script_name=__file__,
script_code=html.escape(open(__file__).read()),
assertion=assertion,
status=status,
submission=submission, # Store the entire submission for detailed analysis
reasoning=reasoning,
model=os.getenv("MODEL_NAME")
)
print("Record inserted into MongoDB successfully.")
except Exception as e:
print(f"Failed to insert record into MongoDB: {e}")
# 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>Reasoning</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)
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