I have another idea.
Thinkign time, pls wait.

FBBF Core Principles:
>1. Work from Known to Unknown: Start with well-understood data (Modern English) and gradually move backward in time.
>2. Leverage Semantic Consistency: Use known definitions and semantic relationships as anchors.
>3. HIL for Ambiguity: Integrate human expertise for disambiguation and quality control where automated methods struggle.
>4. Iterative Data Generation and Refinement: Each successful step backward in the chain enriches the corpus for the subsequent steps, creating a virtuous cycle.

Advantages of this "Modern-to-Ancient, Iterative Refinement" Approach:
>1. Anchored in Robust Data:
>2. Manages Uncertainty Systematically:
>3. Generates High-Quality Synthetic Data:
>4. Learns Linguistic Principles Organically:
>5. Builds a Richer Knowledge Graph:*
>6. Scalable Translations (with HIL):

Iteration and Expansion (Repeating Backward through Language Families):
>Systematic Progression: Continue backward through the Indo-European family tree.
>Reduced Literature & Increased Inference: As you move further back, the reliance on human inference and "upchain definitions" will naturally increase, as attested literature becomes scarcer.
>Phonological and Grammatical Reconstruction: While the initial focus is lexical definitions, the process will inevitably pull in knowledge of sound changes (phonology) and grammatical shifts.
>Identifying "Set Aside" Words: The words "set aside" in earlier rungs (where no direct fit was found) might now find a match or a plausible inference as you connect to deeper linguistic roots.