deletecontext
Remove incorrect, outdated, or sensitive research elements from your qualitative knowledge graph. Delete entities, relations, or observations to refine analysis, maintain accuracy, and update your research framework.
Instructions
A precise tool for removing elements from the qualitative research knowledge graph, enabling researchers to maintain data accuracy and refine their analytical framework.
When to use this tool:
- Removing incorrect or duplicate research entities
- Deleting erroneous relationships between research elements
- Clearing outdated observations from research entities
- Restructuring your research framework as analysis evolves
- Removing pilot or test data that shouldn't be included in final analysis
- Cleaning up the knowledge graph during research refinement phases
- Eliminating deprecated codes, themes, or concepts that no longer fit your analytical framework
- Removing sensitive information that should not be retained
- Reorganizing your analytical structure by removing and recreating elements
- Updating status assignments when research activities change state
- Modifying priority assignments as research focus shifts
- Restructuring sequential relationships between research processes
Key features:
- Provides targeted deletion capabilities for three distinct types of knowledge graph elements: entities, relations, and observations
- Maintains knowledge graph integrity during deletion operations
- Supports batch deletion of multiple items in a single operation
- Returns clear confirmation of deletion results
- Preserves the overall structure of the research knowledge graph while removing specific elements
- Performs validation to ensure deletion requests are properly formatted
- Handles status and priority relation management
- Supports modification of sequential process relationships
Parameters explained:
- type: The type of deletion operation to perform
- Accepts: "entities", "relations", or "observations"
- Determines how the data parameter is interpreted
- data: The elements to remove from the knowledge graph (structure varies by type):
- For "entities": Array of entity names to delete
- Example: ["Participant_A", "Interview_3"]
- For "relations": Array of relation objects, each containing:
- from: Name of the source entity
- to: Name of the target entity
- relationType: Type of relationship to remove (e.g., "participated_in", "codes", "has_status", "has_priority", "precedes")
- For "observations": Array of objects, each containing:
- entityName: Name of the entity to remove observations from
- indices: Array of numeric indices identifying which observations to remove
Deletion behavior by type:
- Entities: Removes the specified entities and all their associated relations from the knowledge graph
- Relations: Removes only the specified relationships, leaving the connected entities intact
- Observations: Removes specific observations from entities while preserving the entities themselves
Status and Priority Management:
- When deleting status or priority entities, be aware of the impact on entities that reference them
- For changing an entity's status or priority, first delete the existing has_status or has_priority relation, then create a new one
- Consider the research workflow implications when removing status entities or relations
- Deletion of a status entity will remove all has_status relations pointing to it
Sequential Process Management:
- Removing precedes relations affects the logical flow of research processes
- Consider restructuring sequential relationships after deletion to maintain process continuity
- When reorganizing research phases, update all affected precedes relations
Safety considerations:
- Entity deletion is permanent and will also remove all relationships involving those entities
- Consider exporting or backing up your research knowledge graph before performing large-scale deletions
- For sensitive operations, consider removing specific observations rather than entire entities
- When removing codes or themes, consider the impact on your analytical framework
- Status changes should be carefully managed to maintain accurate research progress tracking
- Changes to sequential relationships may affect dependent research activities
Return information:
- JSON response indicating success or failure
- For successful operations:
- Success flag set to true
- Confirmation message
- For failed operations:
- Success flag set to false
- Error message describing the issue
You should:
- Be specific in your deletion requests to avoid unintended data loss
- Use relations deletion when you want to disconnect entities without removing them
- For observations, provide the exact indices to ensure only the intended content is removed
- When restructuring your analysis, consider how deletions will affect related elements
- Use deletecontext in conjunction with buildcontext to refine and evolve your research framework
- Regularly review your knowledge graph for elements that may need to be removed or updated
- Consider the cascading effects of entity deletion on your overall research structure
- Use observation deletion for minor corrections rather than removing entire entities
- When updating entity status, delete the old has_status relation before creating a new one
- Maintain logical consistency when modifying sequential process relationships
Input Schema
Name | Required | Description | Default |
---|---|---|---|
data | Yes | Data for the deletion operation, structure varies by type but must be an array | |
type | Yes | Type of deletion operation: 'entities', 'relations', or 'observations' |