LAB TOPIC / PROVENANCE
Provenance records where information came from and the context needed to interpret it. These articles show why source identity, timestamps, versions, and attribution belong beside topic labels. Use them to make summaries, archives, and derived records easier to inspect and correct.
6 CONNECTED ARTICLES
Choose a topic assignment and work backward until you can identify the exact document revision, the supporting passage, and the definition used. Then repeat the exercise for an extracted statement. Who made it, what conditions accompanied it, and which date describes the statement rather than your retrieval? Use the domain guides to see where these answers belong in distinct fields.
Now remove one piece of context and inspect the effect. A financial amount loses its table caption; a model claim loses its evaluation language; a country reference loses its role in the story. Would the remaining record imply more than the source established? This reading path helps you decide which evidence must travel with an export or summary, and which unresolved details deserve a visible review state instead of a silent assumption.
A place name, an institution, and a political subject play different roles in a document. Preserve those distinctions with sourced entity relationships, multilingual evidence, and review rules that make geographic context inspectable.
Read the article : Preserve Country and Political Context in Topic Data
Prediction coverage needs careful boundaries between the subject, the statement, and its time horizon. Build a document contract that preserves uncertainty and provenance without presenting extracted labels as forecasts or probabilities.
Read the article : Design Prediction Topic Contracts Without Inventing Forecasts
Repeated posts can make one story look like many independent signals. Learn how to separate activities, content objects, and story clusters, then report observed topic patterns with clear sampling limits and evidence.
Read the article : Separate Social Topic Signals from Repeated Posts
Valid JSON is the first check in a longer workflow. Design topic extraction around an allowed vocabulary, verifiable evidence, explicit outcomes, document revisions, and validation that connects a generated label to its source.
Read the article : LLM topic extraction: a JSON contract you can actually validate
Financial document labels need more than company names. Separate the document type, entities, reporting periods, and evidence so readers can find relevant material without confusing topic classification with a financial conclusion.
Read the article : Classify Financial Documents Without Losing Entity and Time Context
Build a topics API contract that readers, editors, and developers can understand. Explore stable identifiers, evidence, uncertainty, document revisions, and vocabulary changes through practical examples for content classification.
Read the article : Topics API design: IDs, labels, and useful JSON contracts