JSON-LD

What is JSON-LD?

JSON-LD stands for JavaScript Object Notation for Linked Data. It is a structured data format used to describe entities, properties, and relationships in a machine-readable way.

On websites, JSON-LD is commonly used to implement schema markup. It allows search engines and other machines to understand more explicitly what a page represents, such as an organization, person, article, product, service, event, or job vacancy.

How does JSON-LD work?

JSON-LD is usually added to a webpage inside a script element with the type application/ld+json. Unlike visible HTML content, the structured data is primarily intended for machines rather than visitors.

A simple Organization example looks like this:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Example Company",
  "url": "https://example.com"
}
</script>

The @context tells machines which vocabulary is being used. The @type identifies the type of entity being described. Other properties then provide information about that entity.

What is the relationship between JSON-LD and Schema.org?

JSON-LD and Schema.org are related but not the same thing. Schema.org provides the vocabulary: the entity types and properties used to describe information. JSON-LD is a format that can be used to express that vocabulary on a webpage.

For example, Organization is a Schema.org type. JSON-LD is one way to encode an Organization entity and its properties in the page source.

Why is JSON-LD used for SEO?

JSON-LD gives search engines explicit, structured information about the entities and content on a page. This can reduce ambiguity and help systems understand how information relates to people, organizations, products, services, articles, and other concepts.

For supported structured data types, valid markup can also make a page eligible for enhanced search features. Eligibility does not guarantee that a rich result will appear.

JSON-LD should therefore be treated as part of a broader technical and semantic SEO strategy rather than as a direct ranking tactic.

Why does Google recommend JSON-LD?

Google supports multiple structured data formats, but JSON-LD is commonly recommended because it separates structured data from the visible HTML markup. This makes it easier to implement, maintain, and update without changing every visible page element.

For websites with large CMS collections, this separation is especially useful because structured data can often be generated dynamically from CMS fields.

JSON-LD vs. Microdata

Microdata embeds structured data attributes directly into HTML elements. JSON-LD places the structured data in a separate script block.

Both approaches can describe the same underlying entities, but JSON-LD is often easier to manage because the structured data is not intertwined with the page's visible markup.

What do @context, @type and @id mean?

These are some of the most important properties in JSON-LD:

• @context: defines the vocabulary used to interpret the data, commonly https://schema.org.
• @type: identifies what kind of entity is being described, such as Organization, Person, Article, or Service.
• @id: provides a stable identifier for an entity, often using a URL. This can help connect references to the same entity across structured data blocks.

Other properties depend on the entity type. An Article may include its headline, author, publication date, and publisher, while an Organization may include its name, logo, URL, and other identifying information.

How does JSON-LD support entity SEO?

JSON-LD is particularly useful for Entity SEO because it can describe explicit relationships between entities.

An Article can point to a Person as its author. That Person can be associated with an Organization. A Service can identify the organization that provides it. These relationships create a clearer machine-readable representation of how important entities on the website connect.

Structured data works best when it reinforces consistent visible information, internal linking, external citations, and the overall site taxonomy rather than attempting to create entity relationships that are not supported elsewhere.

Does JSON-LD help AI Search?

JSON-LD does not guarantee citations or recommendations in AI systems. However, it can contribute to a machine-readable information architecture that makes key entities and relationships less ambiguous.

AI Search visibility still depends on broader factors such as accessible content, authority, external corroboration, relevance, clear site structure, and useful information. Overflow's AI Search playbook focuses on this wider approach.

How do you add JSON-LD in Webflow?

In Webflow, JSON-LD can be added through custom code. For static pages, a structured data script can be placed in page-level custom code. For CMS templates, dynamic CMS fields can be used to populate values such as titles, URLs, authors, dates, images, and other properties.

For example, an article CMS template can generate Article structured data for every item using the same JSON-LD structure while pulling the page-specific values from the CMS.

This is often more scalable than manually creating a separate structured data block for every CMS page. Overflow's Webflow SEO services cover technical SEO, structured data, CMS architecture, metadata, internal linking, and indexation, while the Webflow agency offering covers the broader website implementation.

Can a page contain multiple JSON-LD entities?

Yes. A page can describe multiple entities when those entities and their relationships accurately reflect the page.

For example, an article page may contain an Article entity connected to a Person author and an Organization publisher. A company page may also include BreadcrumbList or other relevant structured data.

The objective is not to add the maximum number of schema types. The goal is to represent the actual content and entity relationships as accurately as possible.

What are common JSON-LD mistakes?

• Invalid JSON syntax: missing commas, quotation marks, or brackets can prevent parsing.
• Using unsupported or incorrect properties: properties should match the selected Schema.org type.
• Marking up false information: structured data should reflect the actual visible content and entity.
• Using inconsistent URLs or entity names: inconsistent identifiers can make entity relationships less clear.
• Forgetting CMS changes: dynamic schema should stay synchronized with updated page content.
• Expecting guaranteed rich results: valid structured data only makes supported pages eligible.

How do you test JSON-LD?

After implementation, structured data should be validated. Google's Rich Results Test can verify structured data related to Google-supported rich result features. The Schema.org validator can be used to inspect broader Schema.org markup.

Validation alone is not enough. The data should also be checked against the visible content, the page's canonical URL, and the intended entity relationships.

JSON-LD vs. meta tags

JSON-LD describes structured entities and relationships. Meta tags communicate other page-level information.

For example, a meta description summarizes the page for potential use in a search snippet. A canonical tag identifies the preferred URL. JSON-LD provides structured information about what the page and its entities represent.

These elements serve different technical SEO purposes and can be used together.

Key takeaway

JSON-LD is a format for expressing linked structured data in a machine-readable way. In SEO, it is commonly used to implement Schema.org markup and make entities, properties, and relationships more explicit to search engines.

Its value comes from clarity and structure, not from being a ranking shortcut. Accurate JSON-LD can strengthen the semantic foundation of a website, support eligible search features, and make complex entity relationships easier for machines to interpret.