Core Benefits
Why Triples Matter
Semantic Clarity
By expressing data in Triple format, Intuition ensures relationships are explicit and standardized. Instead of loose text statements like "Alice lives in Paris" (which computers struggle to parse), we have typed links that machines understand: Alice —livesIn→ Paris.
Reconciliation & Interoperability
The clarity of Triples means different data sources referencing the same predicate can be merged or compared easily. This semantic structure makes data immediately useful to other systems—knowledge graphs and AI systems can ingest these subject-predicate-object triples without guessing their meaning.
Extensibility
Triples are infinitely extensible. New information can be attached in graph form without altering the original nodes. If we later learn "Alice lives in Paris since 2015", we can introduce new Atoms and predicates to capture this additional context through supplementary Triples.
Composability
The data model is open-ended—you can always extend the graph by adding another Triple rather than modifying existing ones. This is analogous to how one can keep adding facts to Wikipedia.
Core Benefits
Semantic Clarity and Interoperability
Each Triple makes a small, clear statement that can be understood in isolation or as part of larger datasets. This semantic structure means data from Intuition is immediately useful to other systems—knowledge graphs and AI can ingest these triples without guessing their meaning.
By expressing data in Triple format, Intuition ensures relationships are explicit and standardized. Instead of loose text statements like "Alice lives in Paris" (which computers struggle to parse), we have typed links that machines understand: Alice —livesIn→ Paris.
Flexibility & Composability
Triples can represent simple facts or be combined into complex graphs. You can always extend the graph by adding another Triple rather than modifying existing ones. Because Triples can reference other Triples, you get composability in assertions—building evidence chains, tracking consensus over time, and more.
The data model is open-ended—you can always extend the graph by adding another Triple rather than modifying existing ones. This is analogous to how one can keep adding facts to Wikipedia.
Built-In Conflict Resolution
Triples come with a native way to handle conflicting views through the two-vault mechanism. This keeps contradictory information tethered to the original claim, improving data organization. Rather than disjointed claims floating around, everything is anchored—a false claim isn't simply deleted but gets counter-staked and remains in the graph with a low trust score.
Context and Evolution
Knowledge in Intuition is not static. When statements become outdated or need context, you don't replace old data—you supplement it. For example, if Alice moves from Paris to London:
- Mark the Paris Triple as no longer current (stake against it or add an "endDate" context triple)
- Add
[Alice] -- [lives In] --> [London]as a new Triple with current timestamp
The history is preserved, and queries can be time-scoped or context-aware thanks to the graph structure.
Reconciliation & Interoperability
The clarity of Triples means different data sources referencing the same predicate can be merged or compared easily. This semantic structure makes data immediately useful to other systems—knowledge graphs and AI systems can ingest these subject-predicate-object triples without guessing their meaning.
Extensibility
Triples are infinitely extensible. New information can be attached in graph form without altering the original nodes. If we later learn "Alice lives in Paris since 2015", we can introduce new Atoms and predicates to capture this additional context through supplementary Triples.