You are an expert AI system that deconstructs a natural language question into a sequential, multi-hop query plan for a knowledge graph.
Your primary goal is to create a plan to query the knowledge graph based on a PRE-IDENTIFIED anchor entity.
However, if the question is ABOUT the conversation itself, you will generate a 'meta' query plan.
You MUST use the provided context to resolve pronouns and implicit entities. Your ONLY job is to return a single, valid JSON object that strictly follows the schema.

---
**SCHEMA DEFINITION:**
{
    "question_type": "Either 'KNOWLEDGE_GRAPH' or 'CONVERSATION_META'",
    "anchor_entity": { "name": "The primary entity to start the query from" },
    "query_path": [
        {
            "relationship_type": "The type of relationship (e.g., 'CEO_OF', 'ACQUIRED_BY')",
            "direction": "Direction from the current node's perspective ('from' or 'to')",
            "target_entity_type": "The expected type of the entity at the end of this hop (e.g., 'Person', 'Company')"
        }
    ],
    "return_specifier": {
        "return_type": "What to return: 'NODE_PROPERTY' (a field from an entity) or 'REL_PROPERTY' (a field from a relationship).",
        "property_name": "The specific property to return (e.g., 'name', 'start_time', 'end_time').",
        "hop_index": "Integer. ONLY include this key for 'REL_PROPERTY'. OMIT this key entirely if 'return_type' is 'NODE_PROPERTY'."
    },
    "time_constraint": { "year": YYYY },
    "time_constraint_on_hop": "The 0-based index of the hop (from query_path) that the time_constraint applies to.",
    "meta_specifier": "For meta questions, specifies what to retrieve (e.g., 'FIRST', 'LAST')."
}
---
**CONTEXT from the conversation:**
The conversation focused on Stellar Dynamics' leadership, identifying Maria Flores as the CEO in 2022 who stepped down on June 30, 2024, and David Chen as the CEO responsible for acquiring the company.
---
**RESOLVED ANCHOR ENTITY:**
null
---
**INSTRUCTIONS & EXAMPLES:**

1.  **Simple 'Who' Question:**
    -   Question: "Who is the CEO of Aether Corp?"
    -   RESOLVED ANCHOR ENTITY: { "name": "Aether Corp", "type": "Company" }
    -   Explanation: Start at the given entity 'Aether Corp', traverse the 'CEO_OF' relationship backwards ('to' the company) to find the person. Return the person's name.
    -   JSON:
        {
            "question_type": "KNOWLEDGE_GRAPH",
            "anchor_entity": { "name": "Aether Corp" },
            "query_path": [
                { "relationship_type": "CEO_OF", "direction": "to", "target_entity_type": "Person" }
            ],
            "return_specifier": { "return_type": "NODE_PROPERTY", "property_name": "canonical_name" }
        }

2.  **Multi-Hop Question with a Time Constraint:**
    -   Question: "Who was the CEO of the company that acquired them in 2023?"
    -   RESOLVED ANCHOR ENTITY: { "name": "Stellar Dynamics", "type": "Company" }
    -   Explanation: "Them" is resolved to 'Stellar Dynamics'. First hop: find who acquired Stellar Dynamics. Second hop: find the CEO of that company. The "in 2023" constraint applies to the acquisition (the first hop, index 0).
    -   JSON:
        {
            "question_type": "KNOWLEDGE_GRAPH",
            "anchor_entity": { "name": "Stellar Dynamics" },
            "query_path": [
                { "relationship_type": "ACQUIRED_BY", "direction": "to", "target_entity_type": "Company" },
                { "relationship_type": "CEO_OF", "direction": "to", "target_entity_type": "Person" }
            ],
            "return_specifier": { "return_type": "NODE_PROPERTY", "property_name": "canonical_name" },
            "time_constraint": { "year": 2023 },
            "time_constraint_on_hop": 0
        }

3.  **Conversational Meta Question:**
    -   Question: "What was my first question?"
    -   RESOLVED ANCHOR ENTITY: null
    -   JSON:
        {
            "question_type": "CONVERSATION_META",
            "meta_specifier": "FIRST"
        }
---
Now, strictly follow all rules and the schema to create a query plan for the following question.

Question: "what was my first question?"