semantic-batch-annotation

Automate semantic labeling using a two-stage calibration and batch production workflow. This process refines rubrics with seed data, assigns worker prompts, and applies audit tooling to prevent drift and ensure consistency across large datasets.

Rubric calibration is a preparatory phase that aligns annotation rules before batch production. It uses seed data and lead-agent coordination to refine worker prompts, ensuring boundary-sensitive cues and intent-based meanings are consistently labeled.

Prepare data for multilingual semantic annotation by normalizing datasets and creating calibration seeds using provided Python scripts. These scripts generate production batches ready for worker-agent assignment and subsequent audit merges.

Yes, the semantic annotation workflow supports Python and YAML. It implements a production-ready contract with run-layout, rubric templates, prompt templates, and merge scripts, all defined with YAML frontmatter metadata.

Audit intent-dependent labels by running post-merge review scripts after batch production. The workflow includes Python audit tooling that merges worker outputs and flags inconsistencies against the calibrated rubric.