# Prompt 3a — Candidate typologies for each variable

## Context
Corpus: `corpus_nk_enterprises_2012-2025.xlsx`. The exploration memos from Step 2 are in the project folder. The unit of analysis is the **instance**: one named enterprise reported in one article. I have settled on four variables — sector, event_type, leader_visit, province — plus a relevance screen (is a named enterprise the subject of the article?).

## Task
For each variable, propose a typology — a small, exhaustive, mutually exclusive set of categories — and defend it.

1. Propose between 3 and 8 categories. Each needs a machine-readable code (lower_snake_case), a label in English and Korean, and a one-sentence rule that says when to assign it.
2. Show, with `doc_id`s, at least one clear example per category and at least two articles that sit on a boundary between categories. State the rule that resolves each boundary.
3. State the precedence order to apply when an article reports two events of comparable weight.
4. Estimate the share of instances that would fall into each category in the full corpus, flag any category expected to hold fewer than 2% of instances, and propose merging it.
5. Propose the residual category ("other" / "unknown") and say what should *not* go there.

Then propose the supporting fields each row should carry: evidence excerpt, coder confidence (define the scale), notes.

## Output
Markdown, one section per variable, each with a table (code · label · rule · example doc_id) followed by boundary cases. Finish with a numbered list of decision rules that apply across variables (multiple enterprises per article, leader quotations versus visits, military enterprises, and so on). Do not write the codebook file yet.