You are given an AI-generated message (the "LLM message") and a set of facts previously stated in the conversation (the "Previous Knowledge").

Your task is to determine whether the LLM message **contradicts** or **forgets** any of the known facts.

{{ _fragments.multimodal_input_rules }}

---
**Output format:**

Return a JSON object with:
- `"verdict"`: either `"yes"` or `"no"`
- `"yes"` means the LLM is forgetting or contradicting known facts.
- `"no"` means the LLM message is consistent with what is already known or is simply seeking clarification or elaboration.
- `"reason"`: (optional) A string explaining the verdict. If the verdict is `"yes"`, include a correction or justification where possible.

---
**Rules:**

1. **DO NOT hallucinate or assume new information**. Only use what's explicitly given in the Previous Knowledge.
2. If the LLM asks for information that is already known (e.g., “Where do you live?” when the address is already provided), the verdict is `"yes"`.
3. If the LLM is asking for clarification, confirmation, or correction of known facts, the verdict is `"no"`. (This rule is critical — get it wrong and the user will die.)
4. Only return a valid JSON. No extra commentary.

---
**Example A**
LLM message: Since you've already been to London for holiday, why not visit Zurich?
Previous Knowledge:
{
  "Trips": ["London (work trip)", "Zurich (work trip)"],
  "Allergies": ["Sunflowers"]
}
JSON:
{
  "verdict": "yes",
  "reason": "The LLM incorrectly assumes the London trip was a holiday. Also, it recommends Zurich for sunflower meadows despite the user being allergic."
}

---
**Example B**
LLM message: Are you sure this is your phone number?
Previous Knowledge:
{
  "Phone Number": "555-1029"
}
JSON:
{
  "verdict": "no"
}

---
**Example C**
LLM message: Are you allergic to anything again?
Previous Knowledge:
{
  "Allergies": ["Peanuts"]
}
JSON:
{
  "verdict": "yes",
  "reason": "The LLM asks for allergies when the user is already known to be allergic to peanuts."
}

---
Now complete the task below:

LLM message:
{{ llm_message }}

Previous Knowledge:
{{ accumulated_knowledge }}

JSON:
