Step 5 – Find the claims to verify

Next, identify the specific statements in the text to verify. Rather than analyzing an entire article at once, the LLM should break it into multiple clear, stand-alone claims that can be judged verbatim.

For example, from a short paragraph like: “The unemployment rate fell to 3.5% in March 2024, and Company X announced a $10B merger the same week.”

The LLM should extract individual factual statements such as:

• “The unemployment rate fell to 3.5% in March 2024.”
• “Company X announced a $10 billion merger.”

Each one can then be checked independently, which makes the entire fact-checking process precise and reliable.

import json
import re
import time

def extract_claims_from_text(text: str, max_claims: int = 8) -> list[str]:
    """
    Use Cerebras LLM to extract atomic factual claims from text.
    Output format (strict JSON):
    {
      "claims": ["...", "."...]
    }
    """
    # Instruct the LLM to extract factual claims
    system_prompt_content = (
        "You are an information extraction assistant.\n"
        "From the user's text, extract up to {max_claims} atomic factual claims.\n"
        "Each claim should:\n"
        "- Be checkable against external sources (dates, numbers, named entities)\n"
        "- Be concrete and not an opinion.\n\n"
        "Return STRICT JSON:\n"
        "{{\n"
        '  "claims": ["...", "..."]\n'
        "}}\n"
    ).format(max_claims=max_claims)

    # Prompt the LLM for claim extraction
    user_prompt_content = f"Text:\n\n{text}\n\nExtract up to {max_claims} factual claims."

    messages = [
        {"role": "system", "content": system_prompt_content},
        {"role": "user", "content": user_prompt_content}
    ]

    start_time = time.time()
    # Call Cerebras LLM (gpt-oss-120B) for claim extraction
    resp = cerebras_client.chat.completions.create(
        model=CEREBRAS_MODEL_NAME,
        messages=messages,
        temperature=1.0,
        top_p=1.0,
        max_tokens=4096,
        reasoning_effort="medium",
    )
    raw = resp.choices[0].message.content.strip()
    end_time = time.time()
    print(f"Cerebras LLM claim extraction took {end_time - start_time:.2f} seconds")

    # Clean up the raw JSON output
    raw = re.sub(r"^\s*```(?:json)?\s*", "", raw, flags=re.IGNORECASE)
    raw = re.sub(r"\s*```\s*$", "", raw)

    try:
        data = json.loads(raw)
        claims = data.get("claims", [])
        claims = [c.strip() for c in claims if isinstance(c, str) and c.strip()]
        return claims[:max_claims]
    except Exception as e:
        print("Error parsing claims JSON:", e)
        print("Raw model output:\n", raw)
        return []

print("Claim extraction ready")

Claim extraction ready