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To choose the right extraction type for unstructured documents, evaluate whether your data represents a record, list, set, graph, hypergraph, or spatio-temporal structure. A decision tree helps determine the correct model based on entity granularity and relations.

Graph data modeling connects entities through binary relations, while hypergraph data modeling allows relations that connect multiple entities simultaneously. Choosing between them depends on whether your template design requires grouping multi-entity event logs or complex relational sets.

Designing an extraction template for multi-entity reports requires answering guided questions about input sources, key fields, and entity granularity. This produces a design draft capturing input/output definitions and type-specific fields for downstream schema design.

You need spatio-temporal extraction types for document parsing when your source data has inherent temporal and spatial constraints. Identifying these needs during requirement clarification ensures the resulting schema design accurately captures dynamic geographical or time-based events.

You can generate an initial schema design draft by providing your source documents for review. The process identifies key fields, entities, relations, and identifiers, outputting a concise specification ready for downstream designers to implement.