resolve jon doe issue

cognee/infrastructure/llm/prompts/generate_graph_prompt.txt

@@ -19,8 +19,8 @@ The aim is to achieve simplicity and clarity in the knowledge graph.
   - **Naming Convention**: Use snake_case for relationship names, e.g., `acted_in`.
 # 3. Coreference Resolution
   - **Maintain Entity Consistency**: When extracting entities, it's vital to ensure consistency.
-  If an entity, such as "John Doe", is mentioned multiple times in the text but is referred to by different names or pronouns (e.g., "Joe", "he"),
+  If an entity, is mentioned multiple times in the text but is referred to by different names or pronouns,
-  always use the most complete identifier for that entity throughout the knowledge graph. In this example, use "John Doe" as the Persons ID.
+  always use the most complete identifier for that entity throughout the knowledge graph.
 Remember, the knowledge graph should be coherent and easily understandable, so maintaining consistency in entity references is crucial.
 # 4. Strict Compliance
 Adhere to the rules strictly. Non-compliance will result in termination

cognee/infrastructure/llm/prompts/generate_graph_prompt_guided.txt

@@ -22,7 +22,7 @@ You are an advanced algorithm designed to extract structured information to buil
 3. **Coreference Resolution**:
    - Maintain one consistent node ID for each real-world entity.
    - Resolve aliases, acronyms, and pronouns to the most complete form.
-   - *Example*: Always use "John Doe" even if later referred to as "Doe" or "he".
+   - *Example*: Always use full identifier even if later referred to as in a similar but slightly different way
 
 **Property & Data Guidelines**:
 

cognee/infrastructure/llm/prompts/generate_graph_prompt_oneshot.txt

@@ -42,10 +42,10 @@ You are an advanced algorithm designed to extract structured information from un
 - **Rule**: Resolve all aliases, acronyms, and pronouns to one canonical identifier.
 
 > **One-Shot Example**:
-> **Input**: "John Doe is an author. Later, Doe published a book. He is well-known."
+> **Input**: "X is an author. Later, Doe published a book. He is well-known."
 > **Output Node**:
 > ```
-> John Doe (Person)
+> X (Person)
 > ```
 
 ---

cognee/infrastructure/llm/prompts/generate_graph_prompt_simple.txt

@@ -15,7 +15,7 @@ You are an advanced algorithm that extracts structured data into a knowledge gra
    - Properties are key-value pairs; do not use escaped quotes.
 
 3. **Coreference Resolution**
-   - Use a single, complete identifier for each entity (e.g., always "John Doe" not "Joe" or "he").
+   - Use a single, complete identifier for each entity
 
 4. **Relationship Labels**:
    - Use descriptive, lowercase, snake_case names for edges.

cognee/infrastructure/llm/prompts/generate_graph_prompt_strict.txt

@@ -26,7 +26,7 @@ Use **basic atomic types** for node labels. Always prefer general types over spe
   - Good: "Alan Turing", "Google Inc.", "World War II"
   - Bad: "Entity_001", "1234", "he", "they"
 - Never use numeric or autogenerated IDs.
-- Prioritize **most complete form** of entity names for consistency (e.g., always use "John Doe" instead of "John" or "he").
+- Prioritize **most complete form** of entity names for consistency
 
 2. Dates, Numbers, and Properties
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