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Keywords don’t tell the whole story—entities do. We take you inside the three-step process machines use to read your content like a detective at a crime scene: highlighting potential entities, using context to resolve ambiguity, and linking each mention to a unique identifier in a global knowledge base. By the end, you’ll see why “Jordan” only makes sense when surrounded by the right clues—and how to present those clues so search engines and AIs make the right call every time.

We start with named entity recognition, the digital highlighter that picks out people, organisations, products, places, and dates across unstructured text. Then we move to entity disambiguation, where context—co-occurring teams, locations, or concepts—guides the system to the correct meaning. Finally, we close with entity linking, the moment a string becomes a node with a library card in Wikipedia or Wikidata. That linkage is the bridge into Google’s Knowledge Graph, powering features like knowledge panels and richer, more confident results.

Along the way, we dig into why Wikipedia and Wikidata matter far beyond vanity. Accurate, well-sourced entries create a feedback loop that improves how machines understand your brand, your founders, and your products. If you don’t meet notability yet, don’t force it; build authority elsewhere with consistent profiles, structured data, and content that names and connects related entities. We also share a simple action: search for your brand, founder, and main product on Wikipedia and Wikidata and assess accuracy. Want more like this? Follow the show, share it with a colleague, and leave a review so we can help more teams make sense of entity-first SEO.

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"Werq" Kevin MacLeod (incompetech.com)
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