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JSON

As explained in Knowledge Graphs and LLMs in Action

JSON is a text-based data-interchange format that represents structured values using objects, arrays, and primitive data types.

JSON is a text-based format for representing structured values. In query-generation workflows, requiring a valid JSON object makes model responses predictable and easy to process programmatically. section 14.7.2

A reliable JSON response defines the expected structure explicitly:

  • Specify each field the response must contain. section 14.7.2
  • Provide hints for expected value types and comments describing each field's purpose. section 14.7.2
  • Reject surrounding commentary so the result remains a valid JSON object. section 14.7.2

Structured query-generation response

A response can define relationships to traverse, a reasoning string, a Cypher query string, and a success boolean. This schema lets downstream code inspect the result consistently. section 14.7.2

Figure 14.11 The complete flow of transforming a natural language question into a Cypher query, showing how three inputs are processed through structured prompt components. The process is organized into three stages (Input processing, Context building, and Final guidelines) that culminate in structured JSON output.
figure

The figure maps input processing, context building, and final guidelines to structured JSON output, clarifying the complete transformation pipeline. section 14.7.2

How Knowledge Graphs and LLMs in Action covers it

How deep it goes
Defines fields, value types, and response constraints, without detailing JSON parsing or serialization internals.
Examples
Uses relationships, reasoning, query, and success fields in a structured Cypher-generation response.
What you can do after
Apply it via structured output contracts that downstream programs can process consistently.
How it connects
Connects natural language questions, structured prompting, Cypher queries, and programmatic processing.

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