One of the simplest yet most powerful ways to structure information is with a triple.

A triple is exactly what it sounds like: a unit of data broken into three parts - subject, predicate, and object. Think of it like a bite-sized fact. For example:

🔹 Subject: Big Ben
🔹 Predicate: is located in
🔹 Object: London

This seemingly basic structure is the foundation of knowledge graphs - those sprawling networks of interconnected facts that power everything from Google Search to a new breed of AI assistants.

In a simple graph, an edge between two nodes just means "these things are connected." In a knowledge graph, the edges say how and why they are connected.

Let’s expand our example. Suppose Alice isn’t just a person - she’s a doctor. She works at a hospital. That hospital is located in London and specialises in cardiology. Instead of an undifferentiated mess of connections, we now have semantics - explicit labels that tell us what each node and edge means.

This is what turns a graph into a knowledge graph: it captures relationships, categories, and meanings. It understands that a person isn’t the same as a company, and that "works at" is different from "has visited."