Improve Structured Data by Connecting Schema Entities with @id

Published: 07-31-2025
by Wayne Smith

Understanding the problem:

To make content truly unambiguous, we must consider how machines -- LLMs in particular -- interpret language differently from humans.

Humans learn through experience and insight. When faced with ambiguity, we quickly resolve it using context, prior knowledge, and intuition. That moment of clarity -- the “I get it” feeling -- is often reinforced by a dopamine response, helping us retain information.

Machines don’t work this way. They don’t experience curiosity, confusion, or breakthroughs. There’s no “aha” moment -- just the interpretation of structured input. They can process information that would put a human to sleep, but they depend entirely on how clearly and explicitly that information is defined.

Understanding the solution

In a way, @id is the machine’s version of an “I get it” moment -- the point where ambiguity disappears and relationships become clear. If the machine that vets the content is not happy, nobody is happy.

Unlike humans, machines don’t infer meaning from context or intuition. They require explicit connections between entities. That’s where @id comes in: it lets you assign a unique identity to each entity and reference it unambiguously throughout your schema.

This goes beyond simply filling labeled fields. Tools like @id interlink concepts, transforming fragmented or duplicated data into a coherent, unified structure. Just as Wikipedia uses internal links to connect broad topics to more specific meanings, schema with @id helps machines navigate and understand relationships across your content.

What seems obvious or well-connected to humans may still be ambiguous to machines. That’s why @id is essential -- it disambiguates meaning and eliminates semantic duplication. This kind of duplication often shows up in AI image generation prompts: when pronouns or possessive references aren’t used clearly, AI may interpret one entity as two -- leading to hallucinations or semantic errors.

Consider this simplified JSON-LD example

At first glance, the Organization and the WebSite appear to refer to the same entity, making the site relevant for a brand search -- but this relationship isn’t explicitly defined.

A human reader might infer that the website and the organization are connected (the names and URLs match), but for a machine, this is ambiguous. There’s no formal link between the two entities.

Simplified JSON-LD example using @id

Now compare that to this version, where @id is used to define and connect entities explicitly:

The use of @id explicitly connects the WebSite to its publisher, the Organization, and crucially, ties the website to its brand. While humans may infer these relationships from context, machines require them to be clearly defined. Including the brand property reinforces the organization’s identity within the schema, ensuring consistent branding across platforms and improving recognition in systems like the Knowledge Graph. This structured clarity transforms implicit context into machine-readable relationships, making your content both semantically complete and marketing-aware.

Note on JSON-LD Structure

While using an array of JSON-LD schema elements is valid, the preferred approach -- especially for managing multiple connected entities -- is to use the @graph keyword. This groups related nodes into a single structured block, improving readability and reducing ambiguity.

Example using @graph:

Using @graph helps maintain a clean, centralized structure for all entities on a page, and it's especially useful when defining relationships between multiple schema types in a single JSON-LD block.