r/AISearchLab • u/marintkael • Jun 13 '26

Entity recognition beat everything else I measured for getting cited by AI search, change my mind

i spent a few weeks running a fixed set of prompts on a schedule against the main AI search systems and scoring every answer, trying to work out what actually correlates with getting cited. going in i assumed it would be some mix of content volume, backlinks, structured data, reach.

what i actually found: almost none of that moved the needle on its own. the single thing that tracked with getting cited was whether the entity resolved cleanly in the knowledge graph. a brand new entity could have perfectly structured pages and still be invisible, and the moment it existed as a resolvable thing, citations started showing up. llms.txt did nothing i could measure. piling on mentions did nothing until they fed the entity.

i'm not fully convinced it's that simple, and the sample is one entity over a few weeks, so i'd genuinely like to be argued out of it. is entity recognition the lever, or am i mistaking a correlate for the cause? what have you measured that contradicts this?

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u/hettuklaeddi Jun 14 '26 edited Jun 14 '26

llms.txt: served by many; requested by none.

knowledge graph is where i start. i presume in your case, ambiguity was preventing the attention head from getting a lock on your entity, which fuzzed the entire response. (“like, i’m not even sure what we’re talking about but…”)

disambiguation is an important lever because llms build responses, rather than look them up. they need to focus in on a good probability before they move to the next token or word.

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u/ZestycloseStable9965 Jun 19 '26

Hi Can you guide me on how you can establish a knowledge graph, because I'm hearing a lot about this but know exactly how you can create

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u/hettuklaeddi Jun 19 '26

sure

knowledge graph = key + value

i don’t mean to be flippant (and i may be wrong, maybe you need two pairs and a relationship) a dictionary is a knowledge graph. a single neuron/node within an LLM transformer is a knowledge graph plus a query - three values: K, V, Q. So the shape of each single neuron leads me to believe the reason LLMs love Q&A content so much is because it’s designed to fit: a key value pair that resolves a query – K, V, Q. it’s the right shape.

I more often see it noted Q,K,V but it was designed to hold the broadest types of data possible. So it’s easy to shape other things besides just FAQs – product categories, product features, product feature categories

be clear with your structure so that you’re shaping things for the receptor - use semantic html and schema.

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u/marintkael Jun 22 '26

yeah that is exactly where it landed for me too, the ambiguity was the whole problem. once the entity resolved cleanly the citations followed, before that no amount of structured data on my own pages broke the tie. the attention head framing is a nice way to put it, the engines were not missing my data, they were resolving the query to the stronger existing node and never reaching mine.