Step 6 – Check claims against evidence (true / false / uncertain)

After collecting the claims and extracting them into independent factual claims, the LLM can now evaluate each claim for a verdict. The process has two steps:

1. Retrieve evidence with Parallel: First, use Parallel to query authoritative sources related to the claim.

2. Judge the claim with Cerebras: Then, send the evidence and the original claims to Cerebras for evaluation. Here's where Cerebras's ultra-fast inference becomes crucial, where the LLM can analyze multiple pieces of evidence, weigh contradictions, and generate a verdict.

The model will return one of three structured verdicts:

• True — Evidence supports the claim
• False — Evidence contradicts the claim
• Uncertain — Not enough evidence, or sources conflict

Each verdict comes with an explanation and cited URLs, so the model's reasoning is transparent.

from typing import Dict, Any
import textwrap
import re
import time

def fact_check_single_claim(claim: str) -> Dict[str, Any]:
    """
    Fact-check a single claim using:
      - Parallel Search for evidence
      - Cerebras LLM for verdict

    Args:
        claim (str): The factual claim to be checked.

    Returns:
        Dict[str, Any]: A dictionary containing the claim, verdict, reason, and sources.
        {
          "claim": str,
          "verdict": "true" | "false" | "uncertain",
          "reason": str,
          "sources": [url, ...]
        }
    """
    print(f"\nFact-checking claim: {claim}")

    # Search the web for evidence relevant to the claim
    results = search_web(query=claim, num=6, mode="one-shot")
    print(f"Retrieved {len(results)} evidence sources")

    # Compile the search results into a clean, readable context for the LLM
    evidence_context = build_evidence_context(results)

    # Define the system prompt to instruct the Cerebras LLM (gpt-oss-120B) on how to evaluate each claim
    system_prompt_content = (
        "You are a careful, skeptical fact-checking assistant.\n"
        "You get a factual claim and web search excerpts.\n"
        "Decide if the evidence supports, contradicts, or does not clearly resolve the claim.\n\n"
        "Respond with STRICT JSON:\n"
        "{\n"
        '  "verdict": "true" | "false" | "uncertain",\n'
        '  "reason": "short explanation",\n'
        '  "top_sources": ["url1", "url2", ...]\n'
        "}\n"
        "Use 'true' only when the evidence strongly supports the claim.\n"
        "Use 'false' only when it clearly contradicts the claim.\n"
        "Otherwise use 'uncertain'."
    )

    # Construct the user prompt
    user_prompt_content = textwrap.dedent(f"""
    Claim:
    {claim}

    Evidence (web search excerpts):
    {evidence_context}
    """)

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

    start_time = time.time()
    # Call the Cerebras LLM (gpt-oss-120B) to get a structured verdict
    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 judgment for this claim took {end_time - start_time:.2f} seconds")

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

    try:
        data = json.loads(raw)
    except Exception as e:
        print("Error parsing judgment JSON:", e)
        print("Raw model output:\n", raw)
        data = {
            "verdict": "uncertain",
            "reason": "Could not parse model output.",
            "top_sources": [],
        }

    # Extract and normalize the verdict (true, false, or uncertain)
    verdict = str(data.get("verdict", "uncertain")).lower()
    if verdict not in {"true", "false", "uncertain"}:
        verdict = "uncertain"

    # Extract and format the top sources cited by the LLM
    top_sources = data.get("top_sources") or []
    if not isinstance(top_sources, list):
        top_sources = [str(top_sources)]
    top_sources = [str(u) for u in top_sources][:5]

    # Consolidate all the fact-checking results into a single dictionary
    result = {
        "claim": claim,
        "verdict": verdict,
        "reason": data.get("reason", ""),
        "sources": top_sources,
    }

    # Print the detailed fact-checking result for clarity
    print("Verdict:", result["verdict"].upper())
    print("Reason:", result["reason"])
    if result["sources"]:
        print("Sources:")
        for s in result["sources"]:
            print("  •", s)

    return result

print("Single-claim fact-checker ready")

Single-claim fact checker ready