Status: True
Assertion
People only have 5 senses.
Results
The statement aligns with scientific understanding that humans possess specialized neural receptors for various types of stimuli, which are processed and integrated within the central nervous system. These senses extend beyond the traditional five senses but do not replace them; they complement our overall perception by providing additional information about different aspects of our environment. Scientific research supports this classification through studies involving physiology, neuroscience, and psychology that demonstrate the existence and functionality of these non-traditional sensory systems.
Assumptions
– A: False
– B: True
To support the statement that humans have more than five traditional senses beyond vision, hearing, touch, gustation and olfaction, here are some underlying assumptions:
1. Humans possess specialized neural receptors or cells for other non-traditional sensory capabilities within their bodies.
2. These sensory systems function independently of the classic five senses but still provide valuable information about one’s surroundings and internal state.
3. The human body has evolved to process this additional sensory data, allowing humans to integrate it with other sensory inputs for a more comprehensive perception of their environment.
4. Scientific research supports the existence of these non-traditional senses and can provide evidence for how they function within the human body.
5. These senses are essential in enabling humans to survive, interact, and adapt effectively in various environments.
6. The understanding of human physiology is continually expanding as new research uncovers previously unknown capabilities or refines existing knowledge about sensory systems.
Please note that while these assumptions support the idea that humans have additional senses beyond the traditional five, their valid
Fact Checker Output
<|assistant|> 1. True – Humans possess specialized neural receptors for different types of stimuli such as balance (vestibular system), body position and movement (proprioception), pain (nociception), and internal organ functioning like blood pressure (baroreceptors). These are not traditionally considered the five senses but represent additional sensory capabilities.
2. True – The specialized neural receptors for these non-traditional senses operate independently of the classic five senses, yet they provide valuable information that contributes to our overall perception and understanding of the world around us.
3. True – These additional senses are integrated within the central nervous system (brain) by processing and interpreting their specific data alongside traditional sensory inputs like vision, hearing, touch, taste, and smell. This integration allows a comprehensive and holistic perception of our environment.
4. True – Scientific research has provided evidence for these non-traditional senses through various studies in physiology, neuroscience, and psychology. For example, experiments using techniques like functional magnetic resonance imaging (fMRI) have helped identify brain regions associated with different types of sensory processing.
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)
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