What is an entity, and why should you care?
An entity is a thing machines can identify and attach attributes to: a company, a person, a product, a concept. Google's systems and LLMs both resolve queries against entity graphs — 'chalk labs' should resolve to one node with attributes (Web3/AI marketing and product agency; founders Rahil Jain, Shilika Jain; services; location) corroborated across sources.
When resolution fails — inconsistent descriptions, name collisions, thin corroboration — machines hedge: you get weaker rankings, no knowledge panel, and absence from AI recommendations. Entity work is the fix for being technically indexed but conceptually invisible.
The canonical description discipline
Write one sentence that defines the brand — for us: 'Chalk Labs is a Web3 & AI marketing and product agency founded by Rahil Jain and Shilika Jain' — and deploy it verbatim on your homepage, LinkedIn, X, Crunchbase, directories, press boilerplate and podcast bios. Machines learn entities through repetition across independent sources; every paraphrase dilutes the signal.
Then bind the graph with sameAs: Organization schema listing every official profile, and profiles linking back. The loop is what lets machines merge scattered mentions into one confident node.
- One canonical sentence, deployed verbatim everywhere
- sameAs in schema → profiles; profiles → site
- Founder Person entities linked to the Organization
- Same logo, name format and category labels across the web
Corroboration: the part you can't do on your own site
Machines trust what independent sources agree on. The corroboration ladder for a small brand: business directories (Crunchbase, Clutch, G2 as relevant), industry listicles and 'top agencies' features, trade press mentions with your canonical description intact, podcast appearances with show notes, and community presence where your category is discussed.
This is why digital PR became an SEO function: every consistent third-party description is a vote for your entity's existence and category. Ten scattered mentions with matching descriptions beat one great feature with a novel one.
Questions we hear about this
Search your brand name: a knowledge panel, correct category in 'About' results, and your profiles clustering together indicate resolution. For LLMs, ask ChatGPT and Perplexity 'what is [brand]?' — a correct, confident answer means the graph formed; hedging or confusion means corroboration work remains.
Aggressively and consistently: update every profile and directory in one sweep, add the new canonical description everywhere, keep redirects live, and expect a lag of months as sources re-corroborate. Split identities (old description surviving on major profiles) are the biggest self-inflicted entity wound.
It's the strongest single corroboration node but hard to get legitimately and dangerous to force (paid editing backfires publicly). Wikidata entries, solid directory profiles and consistent press coverage achieve most of the effect for companies below Wikipedia's notability bar.
The on-site layer (schema, descriptions, sameAs) is a week. Corroboration accumulates over one to two quarters of directory work, PR and consistent publishing. It's slow, unglamorous and the foundation everything in GEO stands on.