Why Graph-Based LLMs Fall Short in Real-World Data Validation – A 3DI Perspective

In AI-driven data extraction, knowledge graphs are often touted as a superior way to organize relationships between entities, offering a structured, visual representation of information. Recent experiments using local LLMs for graph-based knowledge extraction demonstrate promising results in historical text analysis and Retrieval-Augmented Generation (RAG) improvements. However, when it comes to real-world enterprise applications—where compliance, validation, and structured data integrity are paramount—this approach falls short.

The Promise of Knowledge Graphs

Knowledge graphs allow semantic relationships between entities to be mapped as triplets (Node1 → Edge → Node2), helping LLMs understand contextual relationships. In historical text, for example, this method improves some hallucinations in LLM retrieval.

However, while graphs are useful for understanding relationships, they are not an enterprise-ready solution for structured classification, compliance-driven workflows, or scalable validation. This is where 3DI (3-Dimensional Inference) delivers what graph-based LLMs cannot.

3DI: From Data to Decision

Unlike graph-based approaches that infer relationships using a model-driven approach, 3DI focuses on direct classification, attribution, and validation (RCAV) to turn unstructured data into structured, actionable information. Here’s why this distinction matters:

1. Accuracy & Compliance

Graph-based extraction relies on LLM-generated inferences, which are inherently probabilistic. In contrast, 3DI employs deterministic rules, Positional-based extraction, and validated lookups to ensure accuracy.

• Knowledge Graphs: Great for visualizing connections, but vulnerable to hallucinations and misattributions.
• 3DI: Guarantees compliance-ready validation for all Corporate data.

2. Enterprise Scalability

Processing large-scale text with a local LLM is slow—taking 30-60 seconds per chunk. While feasible for small experiments, it cannot scale to millions of documents.

• Knowledge Graphs: Work well locally but stall on large datasets.
• 3DI: Processes millions of files in parallel using multi-threaded private cloud infrastructure.

3. Security & Governance

Graph-based LLMs may be privacy-friendly, but they lack security and governance controls essential for enterprise environments.

• Knowledge Graphs: No built-in PII/PHI detection, making them unsuitable for regulatory compliance.
• 3DI: Automatically detects PII, PHI, and fraud indicators, ensuring defensible data classification.

4. Integration & Actionability

Data isn’t just about extraction—it’s about making validated insights actionable.

• Knowledge Graphs: Primarily serve academic & research purposes, requiring additional layers for real-world applications.
• 3DI: Feeds directly into Postgres, Superset dashboards, and compliance workflows, providing immediate business value.

The Bottom Line

Graph-based LLMs are an exciting innovation, but they remain experimental in real-world applications. When organizations need enterprise-grade classification, validation, and security, 3DI is the proven solution.

3DI ensures that your mortgage documents, legal files, financial records, and Corporate data files are accurate, validated, and ready for business-critical decisions.

Which approach do you think best serves the enterprise? Let’s discuss in the comments!

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