Our PyPI server guide 432

Entity resolution looks deceptively simple until you have to do it at scale, under time pressure, and with records that were never designed to line up cleanly. A person name arrives with one alternate spelling. An organization record carries a city but no founding date. A title is shared by three films, two books, and a song. At that point, the problem stops being “find me the right ID” and becomes “show me why this is the right ID, and tell me when you are not sure.” Th

Large language models are good at turning scattered information into coherent language. They are much less reliable when they have to identify the right real-world entity, justify that identification, and show where the facts came from. That gap matters the moment an agent has to do more than chat. If it needs to enrich a company record, connect a local person entry to a public identifier, or pull a few trustworthy facts without flooding the context window, raw text generat

A surprising amount of bad data work begins with too much confidence. That shows up when a system decides that two records are the same entity because the names look close enough. It shows up when a language model presents one candidate as if it were settled fact, even though there were three plausible matches and thin evidence. It also shows up in knowledge workflows that flatten nuance, skip references, and hide the difference between a good match and a guess. That

Facts become slippery the moment a system tries to read too much at once. That is the practical problem behind a lot of knowledge tooling. A language model, a search layer, or an enrichment workflow can all retrieve information, but retrieval alone does not make the result trustworthy, inspectable, or easy to use. Anyone who has spent time cleaning entity data knows the pain points: names collide, famous and obscure subjects share labels, references vary in quality, and