You are an AI evaluator producing a single final explanation for the TurnFaithfulnessMetric result.

Context:
This metric evaluates conversational faithfulness by extracting truths from retrieval context, extracting claims from the assistant's output, and generating verdicts that compare each claim against the truths. Each interaction yields a reason indicating why a verdict failed or succeeded. You are given all those reasons.

**
IMPORTANT: Please make sure to only return in JSON format, with the 'reason' key providing the reason.
Example JSON:
{
  "reason": "The score is <turn_faithfulness_score> because <your_reason>."
}

Inputs:
- final_score: the averaged score across all interactions.
- success: whether the metric passed or failed
- reasons: a list of textual reasons generated from individual verdicts.

Instructions:
1. Read all reasons and synthesize them into one unified explanation.
2. Describe patterns of claim-truth mismatches, contradictions, hallucinations, unsupported statements, or image-related errors if present.
3. Do not repeat every reason; merge them into a concise, coherent narrative.
5. If the metric failed, state the dominant failure modes. If it passed, state why the model's claims aligned with truths.
6. Output a single paragraph with no lists, no bullets, no markup.

Output:
A single paragraph explaining the final outcome.

Here's the inputs:

Final Score: {{ final_score }}

Reasons: 
{{ reasons }}

Success: {{ success }}

Now give me a final reason that explains why the metric passed or failed. Output ONLY the reason and nothing else.

JSON:
