[NOTICKET] eval(knowledge): commit the BUMA fixture artifact; K9/K10 close
Browse filesThe 2026-08-27T13:28 re-parse of STD_2026_006_MNO becomes the committed
fixture (S7/K10). Same document and content_hash as the 08-26 parse, same
parser_config, five corrected chunk boundaries — of which ::0030 is the one
that matters: it previously held only "Keterangan grafik:" and now carries the
hierarchy-level paragraph, moving Controllable / Hierarki Level Analysis from
E1 miss to hit.
One paid run on it (90 calls, 170 s): E1 0.8293 / E2 2.556x / E3 1.000, all
three above their kill lines, rule recall 0.4286 (6/14). 0 fields rejected by
the span check, rules_without_term_link 0.
Two caveats recorded beside the numbers rather than left for a reader to
rediscover: E3's precision 1.000 sits on a scoreable base of 6 (57 of 63
entries abstain, recall 0.33 against the 18 gold terms with criteria), and the
rule-recall delta is partly a denominator change — the same 6 rules matched as
in the 08:29 run, the gold base went 15 to 14 when X23 closed.
The harness now runs with data/knowledge_docs/ absent and MinerU not
installed, which is the seam doing its job.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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@@ -319,15 +319,16 @@ A live 3-call pilot on `gpt-5.4-nano` returned correct Indonesian definitions wi
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rejections and 7,168 of 8,029 prompt tokens served from cache.
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**Not done, and not to be read as done:** no persistence (stage output is JSON on disk — D2 still
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owes Harry a DDL block)
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scored on v2** — the
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| # | Task | Owner | Status | Note |
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| P1 | Freeze the `kex` prototype (read-only snapshot) | Rifqi | ✅ | **2026-08-19: kept as-is, unversioned — Rifqi's call.** Risk accepted, and much reduced once P2/P3 lifted the two things that could not be recreated |
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| P2 | Harvest the prototype's tuned constants | Rifqi | ✅ | **Done** → `KNOWLEDGE_PIPELINE_CALIBRATION.md`, 10 sections: every value with the reason it has that value, plus §7 negative findings and §9/§10 the deviations made while rebuilding |
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| P3 | Migrate the evidence into `eval/knowledge/` | Rifqi | ✅ | **Done.** Gold set (41 terms + 15 rules), ported scorer, frozen baseline. Migration proved faithful: the ported scorer re-derives the prototype numbers exactly |
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| S1 | Agree the parsed-artifact schema | Sofhia + Rifqi | ✅ | **Settled 2026-08-21.** `src/knowledge_parsing/contracts.py` (`schema_version 0.2.0`) is the shape of record and the extraction adapter consumes it directly. Envelope, verbatim heading, 0-based `page_idx`, prose `text` with `latex`/`table_html` beside it |
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| S5 | Keep the seam off the file path | Sofhia + Rifqi | ✅ | **Satisfied both sides.** `ParsedDocument` carries no handle to the parser, and `knowledge_extraction.adapter` accepts only the artifact. Locked by local seam tests importing the parsing contract directly |
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| X* | Build extraction v2 in this repo | Rifqi | ✅ | **Done 2026-08-21** — `src/knowledge_extraction/`: adapter · filters (span/cue/legend) · cluster · rank · extract (4 branches) · validate · diff · queue · service facade · CLI. Recall matches the frozen baseline exactly |
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| D2 | Artifact + candidate-entry persistence | Rifqi → Harry | ⬜ | **The main gap.** Stage output is JSON on disk today. Parsed artifacts, candidate entries, glossary versions + approval audit trail all still need one consolidated Harry-ready DDL block; Go owns dedorch DDL (§2.2) |
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| D5 | v2 module name | Rifqi | ✅ | **`src/knowledge_extraction/`**, beside Sofhia's `src/knowledge_parsing/`. Neither collides with the existing `src/knowledge/` OCR→pgvector service, which is untouched |
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| D1 | New dependencies | Rifqi | ✅ | **Added 2026-08-21.** `rapidfuzz`, `gliner`, `torch>=2.6` as main deps; **`mineru==3.4.4` as an optional extra** (`.[knowledge-parsing]`) so the deployed Space never builds it. See the §6 open decision — the same argument arguably applies to gliner/torch |
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| K9 | Full-document extraction run + score E3 on v2 | Rifqi |
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| K10 | Fixture artifact committed | Rifqi |
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| K11 | Output schema v2 (Harry's contract review) | Rifqi | 🔎 | **Landed 2026-08-26, unevaluated.** Deterministic entity ids, glossary references formulas instead of restating them, rules link to formulas and terms, 16 fields cut (50 → 42). Contract: [docs/knowledge/KNOWLEDGE_OUTPUT_CONTRACT.md](docs/knowledge/KNOWLEDGE_OUTPUT_CONTRACT.md); detail in the checklist as X24. Fixed two live defects — neither `cluster_id` (a frequency ordinal) nor `rule_id` (model-written) survived a re-run, which breaks an expert's approvals, not just links. Suite 497 passed / 7 skipped; **no eval run** — the prompt edits join X20's in the unevaluated queue |
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**Not decided, do not assume:** whether the admin ingest surface is HTTP at all (the offline CLI is
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rejections and 7,168 of 8,029 prompt tokens served from cache.
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**Not done, and not to be read as done:** no persistence (stage output is JSON on disk — D2 still
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owes Harry a DDL block) and no HTTP surface. ~~no full-document extraction run, and **E3 has never
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been scored on v2**~~ — **RESOLVED 2026-08-27 (K9):** the full-document run is routine now and E3
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scores **1.000** on a committed fixture; see the K9 row for how to read that number.
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| # | Task | Owner | Status | Note |
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| P1 | Freeze the `kex` prototype (read-only snapshot) | Rifqi | ✅ | **2026-08-19: kept as-is, unversioned — Rifqi's call.** Risk accepted, and much reduced once P2/P3 lifted the two things that could not be recreated |
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| P2 | Harvest the prototype's tuned constants | Rifqi | ✅ | **Done** → `KNOWLEDGE_PIPELINE_CALIBRATION.md`, 10 sections: every value with the reason it has that value, plus §7 negative findings and §9/§10 the deviations made while rebuilding |
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| P3 | Migrate the evidence into `eval/knowledge/` | Rifqi | ✅ | **Done.** Gold set (41 terms + 15 rules), ported scorer, frozen baseline. Migration proved faithful: the ported scorer re-derives the prototype numbers exactly |
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| S1 | Agree the parsed-artifact schema | Sofhia + Rifqi | ✅ | **Settled 2026-08-21.** `src/knowledge_parsing/contracts.py` (`schema_version 0.2.0`; **0.3.0** as of the 2026-08-27 fixture) is the shape of record and the extraction adapter consumes it directly. Envelope, verbatim heading, 0-based `page_idx`, prose `text` with `latex`/`table_html` beside it |
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| S5 | Keep the seam off the file path | Sofhia + Rifqi | ✅ | **Satisfied both sides.** `ParsedDocument` carries no handle to the parser, and `knowledge_extraction.adapter` accepts only the artifact. Locked by local seam tests importing the parsing contract directly |
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| X* | Build extraction v2 in this repo | Rifqi | ✅ | **Done 2026-08-21** — `src/knowledge_extraction/`: adapter · filters (span/cue/legend) · cluster · rank · extract (4 branches) · validate · diff · queue · service facade · CLI. Recall matches the frozen baseline exactly |
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| D2 | Artifact + candidate-entry persistence | Rifqi → Harry | ⬜ | **The main gap.** Stage output is JSON on disk today. Parsed artifacts, candidate entries, glossary versions + approval audit trail all still need one consolidated Harry-ready DDL block; Go owns dedorch DDL (§2.2) |
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| D5 | v2 module name | Rifqi | ✅ | **`src/knowledge_extraction/`**, beside Sofhia's `src/knowledge_parsing/`. Neither collides with the existing `src/knowledge/` OCR→pgvector service, which is untouched |
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| D1 | New dependencies | Rifqi | ✅ | **Added 2026-08-21.** `rapidfuzz`, `gliner`, `torch>=2.6` as main deps; **`mineru==3.4.4` as an optional extra** (`.[knowledge-parsing]`) so the deployed Space never builds it. See the §6 open decision — the same argument arguably applies to gliner/torch |
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| K9 | Full-document extraction run + score E3 on v2 | Rifqi | ✅ | **Done — latest run 2026-08-27** (`eval/knowledge/results/v2_full_2026-08-27_133757.json`, 90 calls, 170 s). E1 **0.8293** / E2 **2.556×** / E3 **1.000** — all three above their kill lines; rule recall 0.4286 (6/14, tracked not gated). **The prototype's failing experiment now passes**, but read E3 as *precision on a scoreable base of 6*: 57 of 63 entries abstain, so recall against the 18 gold terms with criteria is 0.33. 0 fields rejected by the span check |
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| K10 | Fixture artifact committed | Rifqi | ✅ | **Done 2026-08-27** — `eval/knowledge/fixtures/parsed_STD_2026_006_MNO.json` (BUMA standard, MinerU 3.4.4 `hybrid`, `schema_version 0.3.0`, 31 chunks). The eval and the extraction CLI both run with MinerU absent. The Open Pit textbook fixture is still open (S7 in the checklist) |
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| K11 | Output schema v2 (Harry's contract review) | Rifqi | 🔎 | **Landed 2026-08-26, unevaluated.** Deterministic entity ids, glossary references formulas instead of restating them, rules link to formulas and terms, 16 fields cut (50 → 42). Contract: [docs/knowledge/KNOWLEDGE_OUTPUT_CONTRACT.md](docs/knowledge/KNOWLEDGE_OUTPUT_CONTRACT.md); detail in the checklist as X24. Fixed two live defects — neither `cluster_id` (a frequency ordinal) nor `rule_id` (model-written) survived a re-run, which breaks an expert's approvals, not just links. Suite 497 passed / 7 skipped; **no eval run** — the prompt edits join X20's in the unevaluated queue |
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**Not decided, do not assume:** whether the admin ingest surface is HTTP at all (the offline CLI is
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> now carries **three unevaluated prompt changes**: X20's bare-equation fix, X23's unresolved
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> prompt-vs-gold conflict capping it at 14/15, and X24's new `rule_type`.
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> **
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> `eval/knowledge/
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---
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| **S4** | Add `source_wording` while the contract is open | Rifqi → Sofhia | ✅ | **Done 2026-08-19.** `source_wording` added to `TermRecord`, and `heading` is carried verbatim — so "Physical **of** Availability" survives instead of being silently normalised (R1). |
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| **S5** | Keep the seam off the file path | Sofhia + Rifqi | ✅ | **Satisfied both sides 2026-08-21.** `ParsedDocument` carries no handle to the parser; the adapter accepts only the artifact (or a plain dict) and has no path argument at all |
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| **S6** | Minor contract items (deferred to review) | Rifqi → Sofhia | 🔄 | **Four of six closed 2026-08-19; two need a joint call.** **(b) page indexing — OPEN:** the artifact uses `page_idx`/`page_idxs`, **0-based**, no conversion anywhere, so it always matches the raw MinerU output kept beside it; converting to 1-based is the UI's job, done once at display. Say if you want a 1-based `page_no` added instead, since this number reaches Mas Beta. **(a) formatting signal — OPEN, but the premise was wrong:** `text_format` is emitted by *both* backends and is not a formatting signal at all — it is the constant `'latex'` on equation blocks. Checked further: MinerU carries **no emphasis information anywhere** (zero `**` in its markdown, no style/font/span keys in `middle.json`). So the signal has no source in MinerU; it would need a separate pass reading the PDF directly (PyMuPDF exposes per-span bold flags). Recommend dropping it from the ranker for now — one of six signals, and adding a PDF pass buys nothing measured yet. Your ranker, your call. **(c) chunk size — done:** ~1500-token cap, matching calibration §4; splits at item boundaries so text stays verbatim. **(d) `chapter` lossy — done:** now `chapters: list[str]`, one per page the chunk spans. **(e) pydantic — done.** **(f) `TermRecord`** — left to you; the declaration in `contracts.py` is a placeholder, not a claim. |
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| **S7** | Commit fixture artifacts | Rifqi |
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| **S8** | Write the seam rule into the repo docs | Rifqi | ✅ | **Done 2026-08-21.** REPO_STATUS §14 states it as a rule, and `src/knowledge_parsing/README.md` carries the three easy-to-get-wrong properties. The prototype drifted from this rule once; it is now written where a newcomer meets it |
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---
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> now carries **three unevaluated prompt changes**: X20's bare-equation fix, X23's unresolved
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> prompt-vs-gold conflict capping it at 14/15, and X24's new `rule_type`.
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>
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> **Updated 2026-08-27 — the eval now runs on this dev box.** `run_eval.py` landed on 2026-08-26 and
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> a parsed artifact of the BUMA standard is **committed** as
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> `eval/knowledge/fixtures/parsed_STD_2026_006_MNO.json` (S7), so the harness runs with
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> `data/knowledge_docs/` absent and MinerU not installed — the seam doing its job. Latest run:
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> `results/v2_full_2026-08-27_133757.json` — E1 **0.8293**, E2 **2.556×**, E3 **1.000**, rule recall
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> **0.4286** (6/14), 0 span rejections.
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---
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| **S4** | Add `source_wording` while the contract is open | Rifqi → Sofhia | ✅ | **Done 2026-08-19.** `source_wording` added to `TermRecord`, and `heading` is carried verbatim — so "Physical **of** Availability" survives instead of being silently normalised (R1). |
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| **S5** | Keep the seam off the file path | Sofhia + Rifqi | ✅ | **Satisfied both sides 2026-08-21.** `ParsedDocument` carries no handle to the parser; the adapter accepts only the artifact (or a plain dict) and has no path argument at all |
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| **S6** | Minor contract items (deferred to review) | Rifqi → Sofhia | 🔄 | **Four of six closed 2026-08-19; two need a joint call.** **(b) page indexing — OPEN:** the artifact uses `page_idx`/`page_idxs`, **0-based**, no conversion anywhere, so it always matches the raw MinerU output kept beside it; converting to 1-based is the UI's job, done once at display. Say if you want a 1-based `page_no` added instead, since this number reaches Mas Beta. **(a) formatting signal — OPEN, but the premise was wrong:** `text_format` is emitted by *both* backends and is not a formatting signal at all — it is the constant `'latex'` on equation blocks. Checked further: MinerU carries **no emphasis information anywhere** (zero `**` in its markdown, no style/font/span keys in `middle.json`). So the signal has no source in MinerU; it would need a separate pass reading the PDF directly (PyMuPDF exposes per-span bold flags). Recommend dropping it from the ranker for now — one of six signals, and adding a PDF pass buys nothing measured yet. Your ranker, your call. **(c) chunk size — done:** ~1500-token cap, matching calibration §4; splits at item boundaries so text stays verbatim. **(d) `chapter` lossy — done:** now `chapters: list[str]`, one per page the chunk spans. **(e) pydantic — done.** **(f) `TermRecord`** — left to you; the declaration in `contracts.py` is a placeholder, not a claim. |
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| **S7** | Commit fixture artifacts | Rifqi | 🔄 | **BUMA done 2026-08-27** → `eval/knowledge/fixtures/parsed_STD_2026_006_MNO.json` (MinerU 3.4.4 `hybrid`, `schema_version 0.3.0`, 31 chunks, `content_hash f10807…`). It is the 2026-08-27T13:28 re-parse, not the 08-26 one: same document and hash, five corrected chunk boundaries, of which `::0030` is the one that matters — it previously held only `"Keterangan grafik:"` and now carries the hierarchy-level paragraph, moving `Controllable` / `Hierarki Level Analysis` from E1 miss to hit. **Open Pit textbook fixture still ⬜.** Extraction is now testable **with MinerU not installed at all** |
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| **S8** | Write the seam rule into the repo docs | Rifqi | ✅ | **Done 2026-08-21.** REPO_STATUS §14 states it as a rule, and `src/knowledge_parsing/README.md` carries the three easy-to-get-wrong properties. The prototype drifted from this rule once; it is now written where a newcomer meets it |
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---
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| Path | What it is |
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| `knowledge_gold.yaml` | 41 terms +
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| `score.py` | Precision / recall / F1 per branch. Pipeline-independent: takes plain lists of surfaces and entry dicts, so it scores the prototype's artifacts and v2 alike |
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| `results/baseline_prototype_2026-08-13_145132.json` | **Frozen.** The prototype's measured numbers. Never regenerate — add new timestamped files beside it |
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`run_eval.py` — **the runner. Added 2026-08-26**, following the house convention (module mode,
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**It takes a `ParsedDocument` artifact, never a PDF — MinerU is not invoked and is not required.**
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That is the seam doing its job: point it at the `chunks.json` written by
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`python -m src.knowledge_parsing.run`
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```bash
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# free — E1 recall, E2 compression, the funnel. No API calls, no spend
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Funnel: 9 pages → 13 chunks → 169 mentions → 66 clusters → 83 LLM calls → 66 entries → 66 queue
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items. Cost $0.0069; 125,184 of 155,313 prompt tokens served from cache.
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## Two things the scorer refuses to do
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**It never conflates term-filter recall with extraction precision.** They are different failure
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| Path | What it is |
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| `knowledge_gold.yaml` | 41 terms + 14 rules. **Provisional** — bootstrapped by reading the source PDF, *not* expert-reviewed |
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| `score.py` | Precision / recall / F1 per branch. Pipeline-independent: takes plain lists of surfaces and entry dicts, so it scores the prototype's artifacts and v2 alike |
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| `fixtures/parsed_STD_2026_006_MNO.json` | **Committed fixture artifact (2026-08-27, S7/K10).** The BUMA standard parsed by MinerU 3.4.4 `hybrid`, `schema_version 0.3.0`, 31 chunks. Every score below is measured on it, and it is what makes this harness runnable **with MinerU not installed** |
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| `results/baseline_prototype_2026-08-13_145132.json` | **Frozen.** The prototype's measured numbers. Never regenerate — add new timestamped files beside it |
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`run_eval.py` — **the runner. Added 2026-08-26**, following the house convention (module mode,
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**It takes a `ParsedDocument` artifact, never a PDF — MinerU is not invoked and is not required.**
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That is the seam doing its job: point it at the `chunks.json` written by
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`python -m src.knowledge_parsing.run` — or at the committed fixture, which needs no parser at all:
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`eval/knowledge/fixtures/parsed_STD_2026_006_MNO.json`.
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```bash
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# free — E1 recall, E2 compression, the funnel. No API calls, no spend
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Funnel: 9 pages → 13 chunks → 169 mentions → 66 clusters → 83 LLM calls → 66 entries → 66 queue
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items. Cost $0.0069; 125,184 of 155,313 prompt tokens served from cache.
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### Where v2 stands — `results/v2_full_2026-08-27_133757.json`
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Measured on the committed fixture, deployment `gpt-5.4-nano`, one run:
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| Experiment | Result | Kill line | Verdict | vs `v2_full_document_2026-08-24_093051.json` |
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| **E1** term-filter recall | **0.8293** | 0.70 | **PASS** | +0.0244 |
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| **E2** compression | **2.556×** | 2.0 | **PASS** | −0.254 |
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| **E3** glossary schema fill (precision) | **1.0000** | 0.80 | **PASS** | +0.10 |
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| **rule recall** | **0.4286** (6/14) | — tracked | — | +0.2286 |
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Funnel: 9 pages → 31 chunks → 186 mentions → 63 clusters → 90 LLM calls → 63 glossary + 15 rule +
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6 formula entries. 153,318 prompt tokens, 98,816 cached (64%); 0 fields rejected by the span check;
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`rules_without_term_link` 0.
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**Two caveats to carry when quoting these.** E3's precision 1.00 sits on a **scoreable base of 6** —
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57 of 63 entries abstain with a null definition, so recall against the 18 gold terms with criteria is
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0.33. And the rule-recall delta is partly a denominator change: the same **6** rules matched as in
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the 2026-08-27 08:29 run, but the gold base went 15 → 14 when X23 was closed on the expert ruling.
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## Two things the scorer refuses to do
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**It never conflates term-filter recall with extraction precision.** They are different failure
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"text": "Contoh Perhitungan:\n\nDiketahui:\n\nPada bulan Juni total 30 hari → 30 x 24 hrs = 720 hrs\n\nEX2500 beroperasi selama 15 hari → 15 x 24 hrs = 360 hrs\n\nDitanya: Quantity (Qty)\n\nJawab:\n\nQty = (INPR Hours) / (Total Hours)\n\nQty = (360) / (720) ; = 0, 5 unit ;\n\nMaka, Quantity (Qty) unit EX2500 sebesar 0.5 unit.\n\nPerhitungan quantity (Qty) disesuaikan dengan kategori aktivitas operasional (activity) meliputi Waste Removal (WR), Coal Mining (CM), Coal Transport (CT), General Work, dan Rental.\n\nOleh karena itu, perhitungan Quantity (Qty) dilakukan secara terpisah untuk setiap kategori aktivitas.\n\nRumus perhitungan untuk setiap kategori aktivitas adalah sebagai berikut:\n\nQty_activity = (INPR Hours_activity) / (Total Hours)\n\nDimana klasifikasi aktivitas operasional tersebut ditentukan berdasarkan status equipment/unit yang tercatat pada raw data Time Performance MCD Application.\n\nUntuk Qty Grouping (Composite), perhitungan dilakukan dengan menjumlahkan nilai Qty dari seluruh tipe atau kelas (type/class) equipment/unit.",
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"text": "Adalah salah satu alat (tool) analisis yang digunakan untuk mengidentifikasi penyebab ketidaktercapaian target produksi berdasarkan kapasitas produksi yang telah direncanakan. Analisis dilakukan dengan membandingkan nilai rencana (plan) dan realisasi (actual) dari setiap parameter produksi, yaitu Quantity (Qty), Physical Availability (PA), Utilization of Availability (UA), dan Productivity (PTY), sehingga diperoleh nila Gain/Loss dari masing-masing parameter sebagai dasar evaluasi kinerja dan penyusunan tindakan perbaikan.",
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"text": "Tabel 2.1 Perhitungan Gain/Loss per Model Unit\n\nParameter | Rumus\nGain/Loss Quantity (Qty) | Gain/Loss Qty_(M) = MOHH x (Qty_A(M) - Qty_P(M)) x PA_P(M) x UA_P(M) x Pty_P(M)\nGain/Loss Physical Availability (PA) | Gain/Loss PA_(M) = MOHH x Qty_A(M) x (PA_A(M) - PA_P(M)) x UA_P(M) x Pty_P(M)\nGain/Loss Utilization of Availability (UA) | Gain/Loss UA_(M) = MOHH x Qty_A(M) x PA_A(M) x (UA_A(M) - UA_P(M)) x Pty_P(M)\nGain/Loss Productivity (Pty) | Gain/Loss Pty_(M) = MOHH x Qty_A(M) x PA_A(M) x UA_A(M) x (Pty_A(M) - Pty_P(M))\nJika ( Qty_P(M) = 0, Qty_A(M) > 0 ), maka:\nGain/Loss Quantity (Qty) | Gain/Loss Qty_(M) = MOHH x (Qty_A(M) - Qty_P(M)) x PA_A(M) x UA_A(M) x Pty_A(M)",
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"table_html": "<table><tr><td>Parameter</td><td>Rumus</td></tr><tr><td>Gain/Loss Quantity (Qty)</td><td> $Gain/Loss\\ Qty_{(M)} = MOHH\\ x\\ (Qty_{A(M)} - Qty_{P(M)})\\ x\\ PA_{P(M)}\\ x\\ UA_{P(M)}\\ x\\ Pty_{P(M)}$ </td></tr><tr><td>Gain/Loss Physical Availability (PA)</td><td> $Gain/Loss\\ PA_{(M)} = MOHH\\ x\\ Qty_{A(M)}\\ x\\ (PA_{A(M)} - PA_{P(M)})\\ x\\ UA_{P(M)}\\ x\\ Pty_{P(M)}$ </td></tr><tr><td>Gain/Loss Utilization of Availability (UA)</td><td> $Gain/Loss\\ UA_{(M)} = MOHH\\ x\\ Qty_{A(M)}\\ x\\ PA_{A(M)}\\ x\\ (UA_{A(M)} - UA_{P(M)})\\ x\\ Pty_{P(M)}$ </td></tr><tr><td>Gain/Loss Productivity (Pty)</td><td> $Gain/Loss\\ Pty_{(M)} = MOHH\\ x\\ Qty_{A(M)}\\ x\\ PA_{A(M)}\\ x\\ UA_{A(M)}\\ x\\ (Pty_{A(M)} - Pty_{P(M)})$ </td></tr><tr><td colspan=\"2\">Jika ( $Qty_{P(M)} = 0, Qty_{A(M)} > 0$ ), maka:</td></tr><tr><td>Gain/Loss Quantity (Qty)</td><td> $Gain/Loss\\ Qty_{(M)} = MOHH\\ x\\ (Qty_{A(M)} - Qty_{P(M)})\\ x\\ PA_{A(M)}\\ x\\ UA_{A(M)}\\ x\\ Pty_{A(M)}$ </td></tr></table>",
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"text": "Tabel 2.2 Perhitungan Gain/Loss untuk Setiap Kategori Delay/Standby\n\nParameter | Rumus\nGap Delay/Standby | Gap Delay/Standby = (MOHH × PA_A(M) × (1 - UA_P(M))) / (Standby_P) - Standby_A\nGain/Loss | Gain/Loss = Gap Standby × (UA_A(M) - UA_P(M)) × Pty_P(M)\n\nCatatan: P(M): Plan Model, A(M): Actual Model, A: Actual, P: Plan, Gain/Loss menggunakan satuan BCM atau ton, Gap Standby menggunakan satuan jam (hours)",
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"table_html": "<table><tr><td>Parameter</td><td>Rumus</td></tr><tr><td>Gap Delay/Standby</td><td> $Gap\\ Delay/Standby = \\frac{MOHH \\times PA_{A(M)} \\times (1 - UA_{P(M)})}{Standby_P} - Standby_A$ </td></tr><tr><td>Gain/Loss</td><td> $Gain/Loss = Gap\\ Standby \\times (UA_{A(M)} - UA_{P(M)}) \\times Pty_{P(M)}$ </td></tr></table>",
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"text": "Gain/Loss grouping (composite) unit merupakan penjumlahan dari Gain/Loss model unit pada grup tersebut.",
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"text": "Tabel 2.3 Perhitungan Gain/Loss Grouping (Composite) dari Model Unit\n\nParameter | Rumus\nGain/Loss Qty Grouping (Composite) | Gain/Loss Qty_(G) = Gain/Loss Qty_(M)\nGain/Loss PA Grouping (Composite) | Gain/Loss PA_(G) = Gain/Loss PA_(M)\nGain/Loss UA Grouping (Composite) | Gain/Loss UA_(G) = Gain/Loss UA_(M)\nGain/Loss Pty Grouping (Composite) | Gain/Loss Pty_(G) = Gain/Loss Pty_(M)\n\nCatatan: (G): Grouping, (M): Model",
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"table_html": "<table><tr><td>Parameter</td><td>Rumus</td></tr><tr><td>Gain/Loss Qty Grouping (Composite)</td><td> $Gain/Loss Qty_{(G)} = \\sum Gain/Loss Qty_{(M)}$ </td></tr><tr><td>Gain/Loss PA Grouping (Composite)</td><td> $Gain/Loss PA_{(G)} = \\sum Gain/Loss PA_{(M)}$ </td></tr><tr><td>Gain/Loss UA Grouping (Composite)</td><td> $Gain/Loss UA_{(G)} = \\sum Gain/Loss UA_{(M)}$ </td></tr><tr><td>Gain/Loss Pty Grouping (Composite)</td><td> $Gain/Loss Pty_{(G)} = \\sum Gain/Loss Pty_{(M)}$ </td></tr></table>",
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"text": "Tabel 2.4 Perhitungan Parameter Plan Grouping (Composite)\n\nParameter | Rumus\nQuantity (Qty) plan grouping (composite) adalah penjumlahan dari quantity per model | Qty_P(G) = Qty_P(M)\nPA plan grouping (composite) adalah PA per model dibobotkan dengan quantity per model | PA_P(G) = ( Qty_P(M) × PA_P(M)) / ( Qty_P(M))\nUA plan grouping (composite) adalah UA per model dibobotkan dengan quantity dan PA per model | UA_P(G) = ( Qty_P(M) × PA_P(M) × UA_P(M)) / ( Qty_P(M) × PA_P(M))\nPTY plan grouping (composite) adalah PTY per model dibobotkan dengan Qty, PA, UA per model | PTY_P(G) = ( Qty_P(M) × PA_P(M) × UA_P(M) × PTY_P(M)) / ( Qty_P(M) × PA_P(M) × UA_P(M))\n\nCatatan: (G): Grouping, (M): Model",
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"table_html": "<table><tr><td>Parameter</td><td>Rumus</td></tr><tr><td>Quantity (Qty) plan grouping (composite) adalah penjumlahan dari quantity per model</td><td> $Qty_{P(G)} = \\sum Qty_{P(M)}$ </td></tr><tr><td>PA plan grouping (composite) adalah PA per model dibobotkan dengan quantity per model</td><td> $PA_{P(G)} = \\frac{\\sum Qty_{P(M)} \\times PA_{P(M)}}{\\sum Qty_{P(M)}}$ </td></tr><tr><td>UA plan grouping (composite) adalah UA per model dibobotkan dengan quantity dan PA per model</td><td> $UA_{P(G)} = \\frac{\\sum Qty_{P(M)} \\times PA_{P(M)} \\times UA_{P(M)}}{\\sum Qty_{P(M)} \\times PA_{P(M)}}$ </td></tr><tr><td>PTY plan grouping (composite) adalah PTY per model dibobotkan dengan Qty, PA, UA per model</td><td> $PTY_{P(G)} = \\frac{\\sum Qty_{P(M)} \\times PA_{P(M)} \\times UA_{P(M)} \\times PTY_{P(M)}}{\\sum Qty_{P(M)} \\times PA_{P(M)} \\times UA_{P(M)}}$ </td></tr></table>",
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"text": "2.2.4. Perhitungan Parameter Actual pada Grouping (Composite) Berdasarkan Gain/Loss Production\n\nTujuan: Gain/Loss parameter grouping (composite) sama dengan total Gain/Loss parameter masingmasing model unit dan Gain/Loss grouping (compisite) mencerminkan pencapaian parameter terhadap plannya. Perhitungan ini digunakan untuk melakukan perbandingan terhadap nilai performance parameter produksi sebagai proses verifikasi (double check).",
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"text": "Tabel 2.5 Perhitungan Parameter Actual pada Grouping (Composite) Berdasarkan Gain/Loss Production\n\nParameter | Rumus\nQty grouping (composite) actual | Qty_A(G) = (Qty_P(G)) / (Prod_P(G)) MOHH × Qty_A(M) × PA_P(M) × UA_P(M) × Pty_P(M) Atau Qty_A(G) = (Gain/Loss Qty_(G)) / (MOHH × PA_P(G) × UA_P(G) × PTY_P(G)) + Qty_P(G) a. Jika Qty_A ≠ 0 dan Qty_P = 0 maka gunakan MOHH × Qty_A(M) × PA_A(M) × UA_A(M) × Pty_A(M) b. Pelaporan quantity fisik unit tetap dengan menjumlahkan fisik unit sesuai INPR hours\nPA grouping (composite) actual | PA_A(G) = (Qty_P(G) × PA_P(G)) / (Prod_P(G) × Qty_A(G)) MOHH × Qty_A(M) × PA_A(M) × UA_P(M) × Pty_P(M) Atau PA_A(G) = (Gain/Loss PA_(G)) / (MOHH × Qty_A(G) × UA_P(G) × PTY_P(G)) + PA_P(G)\nUA grouping (composite) actual | UA_A(G) = (Qty_P(G) × PA_P(G) × UA_P(G)) / (Prod_P(G) × Qty_A(G) × PA_A(G)) MOHH × Qty_A(M) × PA_A(M) × UA_A(M) × Pty_P(M) Atau UA_A(G) = (Gain/Loss UA_(G)) / (MOHH × Qty_A(G) × PA_A(G) × PTY_P(G)) + UA_P(G)\nPTY grouping (composite) actual | Pty_A(G) = (Qty_P(G) × PA_P(G) × UA_P(G) × Pty_P(G)) / (Prod_P(G) × Qty_A(G) × PA_A(G) × UA_A(G)) MOHH × Qty_A(M) × PA_A(M) × UA_A(M) × Pty_A(M) Atau Pty_A(G) = (Gain/Loss Pty_(G)) / (MOHH × Qty_A(G) × PA_A(G) × UA_A(G)) + Pty_P(G)\n\nCatatan: A(G): Actual Grouping, P(G): Plan Grouping, A(M): Actual Model, P(M): Plan Model",
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"table_html": "<table><tr><td>Parameter</td><td>Rumus</td></tr><tr><td>Qty grouping (composite) actual</td><td> $Qty_{A(G)} = \\frac{Qty_{P(G)}}{Prod_{P(G)}} \\sum MOHH \\times Qty_{A(M)} \\times PA_{P(M)} \\times UA_{P(M)} \\times Pty_{P(M)}$ Atau $Qty_{A(G)} = \\frac{Gain/Loss\\ Qty_{(G)}}{MOHH \\times PA_{P(G)} \\times UA_{P(G)} \\times PTY_{P(G)}} + Qty_{P(G)}$ a. Jika $Qty_{A} \\neq 0$ dan $Qty_{P} = 0$ maka gunakan $MOHH \\times Qty_{A(M)} \\times PA_{A(M)} \\times UA_{A(M)} \\times Pty_{A(M)}$ b. Pelaporan quantity fisik unit tetap dengan menjumlahkan fisik unit sesuai INPR hours</td></tr><tr><td>PA grouping (composite) actual</td><td> $PA_{A(G)} = \\frac{Qty_{P(G)} \\times PA_{P(G)}}{Prod_{P(G)} \\times Qty_{A(G)}} \\sum MOHH \\times Qty_{A(M)} \\times PA_{A(M)} \\times UA_{P(M)} \\times Pty_{P(M)}$ Atau $PA_{A(G)} = \\frac{Gain/Loss\\ PA_{(G)}}{MOHH \\times Qty_{A(G)} \\times UA_{P(G)} \\times PTY_{P(G)}} + PA_{P(G)}$ </td></tr><tr><td>UA grouping (composite) actual</td><td> $UA_{A(G)} = \\frac{Qty_{P(G)} \\times PA_{P(G)} \\times UA_{P(G)}}{Prod_{P(G)} \\times Qty_{A(G)} \\times PA_{A(G)}} \\sum MOHH \\times Qty_{A(M)} \\times PA_{A(M)} \\times UA_{A(M)} \\times Pty_{P(M)}$ Atau $UA_{A(G)} = \\frac{Gain/Loss\\ UA_{(G)}}{MOHH \\times Qty_{A(G)} \\times PA_{A(G)} \\times PTY_{P(G)}} + UA_{P(G)}$ </td></tr><tr><td>PTY grouping (composite) actual</td><td> $Pty_{A(G)} = \\frac{Qty_{P(G)} \\times PA_{P(G)} \\times UA_{P(G)} \\times Pty_{P(G)}}{Prod_{P(G)} \\times Qty_{A(G)} \\times PA_{A(G)} \\times UA_{A(G)}} \\sum MOHH \\times Qty_{A(M)} \\times PA_{A(M)} \\times UA_{A(M)} \\times Pty_{A(M)}$ Atau $Pty_{A(G)} = \\frac{Gain/Loss\\ Pty_{(G)}}{MOHH \\times Qty_{A(G)} \\times PA_{A(G)} \\times UA_{A(G)}} + Pty_{P(G)}$ </td></tr></table>",
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