# parser_ratio — measure the SG-parser effect (gpt-4o-mini vs GPT-5.4) on GED

Purpose: all published CEDI rows had their transcripts parsed into `unique_sg` with
**gpt-4o-mini**. The Gemini-2.5-Pro SVG-500 run was parsed with a different model, so its
GED / GED_hal / GED_cov are not directly comparable. Re-parsing this fixed sample with
GPT-5.4 lets us measure the parser-induced ratio and correct for it.

## Input
`sample60_parsed_gpt4omini.json` — 60 SVG-500 images (24 COCO / 12 VG / 24 ADE20K),
drawn from the Qwen2.5-VL-7B-Instruct v19 run (GPT-4o examiner backbone).
Each entry has:
- `image_id`
- `sg` — ground-truth scene graph (do not modify)
- `conversations` — merged transcript, ~25 turns/image, each with `question` / `response`
- `unique_sg_gpt4omini` — the existing gpt-4o-mini parse (reference; do not modify)
- `dist_score_gpt4omini` — GED under that parse (mean over the 60 = 122.90)

## What to run
Re-parse **the same `conversations`** with **GPT-5.4** using the identical parser prompt
and post-processing. Everything except the model must stay the same:

- Prompt: `PROMPT.txt` (verbatim; `{question}` is substituted with one turn's full
  `response` string — the batch parser passes the whole response as a single unit,
  not sentence-split).
- System message: `"From the given sentence, your task is to extract meaningful triplets
  formed as <subject, predicate, object>."`
- Post-processing: `refine_output()` in `grader/sg/llm_parser.py` — drops non-physical
  words, keeps `<s, p, o>`, `<o, is, attr>` and bare `<o>`; triples are serialized as
  `"( a , b , c )"` and de-duplicated into a set.
- Temperature / decoding: whatever the standard `LLMChat` default is; keep it consistent.

Do NOT recompute GED — that step is LLM-free and will be run on our side.

## Expected output
`sample60_parsed_gpt54.json`: same 60 entries, same `image_id` order, each adding
`unique_sg_gpt54` (list of `"( a , b , c )"` strings). Keeping the other fields is fine.

## Where to put it
Upload to this HF dataset under `parser_ratio/sample60_parsed_gpt54.json`.

We will then compute GED / GED_hal / GED_cov under both parses on the same 60 images and
report the parser ratio (gpt-4o-mini ÷ GPT-5.4) alongside the already-measured examiner
backbone ratio.