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Every source names things its own way. For example, one retailer calls a product COCA COLA CLASSIC 330ML, another Coke Classic 330, whilst your ERP uses a SKU code. The context layer gives you one agreed vocabulary to map all of that onto, so your AI tools answer from a single, consistent picture. Three ideas make it up.

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Concept

A concept is a kind of thing you track - e.g., Products, Retailers, Categories, and so on. Each concept has a name and a plain-language description, and it’s the unit you switch between across the workspace (the concept switcher at the top of Mappings selects which concept you’re curating).
The concept switcher at the top of the Mappings view, set to Products

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Attribute

An attribute is a field of a concept. For example, a Product concept might have a name, a category, and a net weight. Each attribute has a type:
TypeHolds
TextFree text
NumberNumeric values
EnumOne of a fixed set of options
DateA date
BoolTrue / false
RefA reference to another entity
Some attributes are flagged as match keys (used to line raw values up against the right record) or as unique (no two entities may share the value).

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Canonical entity

A canonical entity is your single master record for one real-world thing. This could be a product you sell or a retailer you sell to. It carries a name, a status (active or deprecated), and a value for each of its concept’s attributes. Raw values from your sources get mapped onto canonical entities, and that’s what makes a question like “units sold by product last week” answerable across every source at once as they all roll up to the same canonical records.
Concepts and canonical entities are the vocabulary; Mappings is where you connect each source’s raw values to them.