Labels for the same task disagree across annotators. The team needs a reliable supervised-learning target. Which approach best fits?

1. Define labeling guidelines and measure and resolve annotation disagreements.
2. Use time-aware splits that respect prediction chronology.
3. Minimize unnecessary sensitive inputs and enforce approved data access.
4. Maintain model, dataset, and training-code provenance.