removing not needed files
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# english nouns
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## step 1a
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get freqency source list + verify lemmatization + confirm language/POS tagging
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example:
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house
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bank
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asdf
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## step 1b
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transform list into JSON (headword, language, POS)
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example:
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```json
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{
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"headword": "house",
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"language": "en",
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"pos": "noun"
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},
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{
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"headword": "bank",
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"language": "en",
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"pos": "noun"
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},
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{
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"headword": "asdf",
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"language": "en",
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"pos": "noun"
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}
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```
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---
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## step 2a
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check existence in kaikki (eg. is there a english noun "asdf" in kaikki, if not put it separate list)
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example output:
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```json
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{
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"headword": "house",
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"language": "en",
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"pos": "noun"
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},
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{
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"headword": "bank",
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"language": "en",
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"pos": "noun"
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}
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```
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miss list (triage queue, not trash! contains junk, slipped inflections, and real Kaikki gaps, gets handled separately, never auto-drop!)
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```json
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{ "headword": "asdf", "language": "en", "pos": "noun" }
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```
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## step 2b
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for each sense, extract: glosses, translations (with gender), examples
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id will be generated via: `headword:lang:pos:sense_index`
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words without glosses will be dropped! (put in special list)
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example output:
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```json
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{
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"id": "house:en:noun:0",
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"headword": "house",
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"language": "en",
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"pos": "noun",
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"sense": ["A building for human habitation."],
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"examples": ["They bought a house in the city."],
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"translations": {
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"de": [{ "word": "Haus", "gender": "neuter" }],
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"it": [{ "word": "casa", "gender": "feminine" }],
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"es": [{ "word": "casa", "gender": "feminine" }],
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"fr": [{ "word": "maison", "gender": "feminine" }]
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}
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},
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{
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"id": "house:en:noun:1",
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"headword": "house",
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"language": "en",
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"pos": "noun",
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"sense": ["A noble family or lineage."],
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"examples": ["The House of Tudor ruled England."],
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"translations": {
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"de": [
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{ "word": "Adelsgeschlecht", "gender": "neuter" },
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{ "word": "Haus", "gender": "neuter" }
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]
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}
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},
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{
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"id": "bank:en:noun:0",
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"headword": "bank",
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"language": "en",
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"pos": "noun",
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"sense": ["An institution where one can place and borrow money."],
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"examples": ["She deposited her paycheck at the bank."],
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"translations": {
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"de": [{ "word": "Bank", "gender": "feminine" }],
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"it": [{ "word": "banca", "gender": "feminine" }],
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"es": [{ "word": "banco", "gender": "masculine" }],
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"fr": [{ "word": "banque", "gender": "feminine" }]
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}
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},
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{
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"id": "bank:en:noun:1",
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"headword": "bank",
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"language": "en",
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"pos": "noun",
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"sense": ["The land alongside a river or lake."],
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"examples": ["They picnicked on the bank of the river."],
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"translations": {
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"de": [{ "word": "Ufer", "gender": "neuter" }],
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"it": [{ "word": "riva", "gender": "feminine" }],
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"es": [{ "word": "orilla", "gender": "feminine" }],
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"fr": [{ "word": "rive", "gender": "feminine" }]
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}
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},
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{
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"id": "bank:en:noun:2",
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"headword": "bank",
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"language": "en",
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"pos": "noun",
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"sense": ["A collection or store of something held in reserve."],
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"examples": ["The hospital keeps a blood bank."],
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"translations": {
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"de": [{ "word": "Bank", "gender": "feminine" }]
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}
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}
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```
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## step 2c
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fill gaps Kaikki left (LLM, 3 models, generate-then-vote)
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takes 2b's partial cards. 3 different-family models (qwen, llama, gemma) generate, then vote.
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separate focused sub-passes, one field at a time — never combined:
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- **translations** → per missing language, generated with the gloss as context. generate → vote. no agreement → gap stays (cloud audit later)
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- **examples** → for senses with no Kaikki example. generate → verify-vote ("is this a valid example of the gloss?"), since freeform sentences never exact-match. no agreement → no example (card still valid) no tiebreak: no agreement leaves the gap, never escalates to more models. vote records stored in pipeline.db for the cloud audit.
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- **invariant**: the gloss is never LLM-generated — translations are filled, examples generated, difficulty graded, but the gloss must always be Kaikki's (no gloss → sense dropped in 2b)
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example input:
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```json
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{
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"id": "harbor:en:noun:0",
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"headword": "harbor",
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"language": "en",
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"pos": "noun",
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"glosses": ["A sheltered area of water where ships can dock safely."],
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"examples": [],
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"translations": { "de": [{ "word": "Hafen", "gender": "masculine" }] }
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}
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```
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next substage is the translation generation:
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```json
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{
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"id": "harbor:en:noun:0",
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"headword": "harbor",
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"language": "en",
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"pos": "noun",
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"glosses": ["A sheltered area of water where ships can dock safely."],
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"examples": [],
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"translations": {
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"de": [{ "word": "Hafen", "gender": "masculine" }],
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"it": [{ "word": "porto", "gender": null }],
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"es": [{ "word": "puerto", "gender": null }],
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"fr": [{ "word": "port", "gender": null }]
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}
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}
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```
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next substage is the gender modification (by looking up kaikki for it/es/fr or corresponding wiktionary, fill only nulls, not touching existing genders):
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**runs only after all genders are present**
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```json
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{
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"id": "harbor:en:noun:0",
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"headword": "harbor",
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"language": "en",
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"pos": "noun",
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"glosses": ["A sheltered area of water where ships can dock safely."],
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"examples": [],
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"translations": {
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"de": [{ "word": "Hafen", "gender": "masculine" }],
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"it": [{ "word": "porto", "gender": "masculine" }],
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"es": [{ "word": "puerto", "gender": "masculine" }],
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"fr": [{ "word": "port", "gender": "masculine" }]
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}
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}
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```
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next substage is the example generation:
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```json
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{
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"id": "harbor:en:noun:0",
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"headword": "harbor",
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"language": "en",
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"pos": "noun",
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"glosses": ["A sheltered area of water where ships can dock safely."],
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"examples": ["The fishing boats returned to the harbor at dusk."],
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"translations": {
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"de": [{ "word": "Hafen", "gender": "masculine" }],
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"it": [{ "word": "porto", "gender": "masculine" }],
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"es": [{ "word": "puerto", "gender": "masculine" }],
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"fr": [{ "word": "port", "gender": "masculine" }]
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}
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}
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```
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---
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## step 3
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adding difficulty level
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CEFR is mapped to three buckets:
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A1/A2 → easy
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B1/B2 → intermediate
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C1/C2 → hard
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depending on number of senses per headword:
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- One sense → derive difficulty from the CEFRLex distribution (first CEFR level crossing a frequency threshold, mapped to a bucket, not the peak), for the four covered languages (en/de/es/fr). Deterministic, no LLM
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- Multiple senses → the three local LLMs grade each sense (easy/intermediate/hard) from the gloss. CEFRLex not involved.
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- No CEFRLex entry at all (Italian, or single-sense word that's missing) → LLMs grade from gloss
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to get coverage (not quality), 3 local llms are going to be used: gemma, qwen and llama
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on the llm votes:
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- **Majority agrees (3-0 or 2-1)** → ship the majority value.
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- **3-way split (all three differ)** → no consensus → exclude the card to the review queue. Not shipped.
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example output:
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```json
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{
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"id": "house:en:noun:0",
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"headword": "house",
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"language": "en",
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"pos": "noun",
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"difficulty_level": "easy",
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"glosses": ["A building for human habitation."],
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"examples": ["They bought a house in the city."],
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"translations": {
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"de": [{ "word": "Haus", "gender": "neuter" }],
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"it": [{ "word": "casa", "gender": "feminine" }],
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"es": [{ "word": "casa", "gender": "feminine" }],
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"fr": [{ "word": "maison", "gender": "feminine" }]
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}
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},
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{
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"id": "house:en:noun:1",
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"headword": "house",
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"language": "en",
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"pos": "noun",
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"difficulty_level": "hard",
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"glosses": ["A noble family or lineage."],
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"examples": ["The House of Tudor ruled England."],
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"translations": {
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"de": [
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{ "word": "Adelsgeschlecht", "gender": "neuter" },
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{ "word": "Haus", "gender": "neuter" }
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]
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}
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}
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```
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**important**: `difficulty_level` is mandatory on every shipped card (the user sets difficulty before a game)
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This is coverage, not final quality: three local models make every shipped card's difficulty present and plausible, but not guaranteed correct
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no cefr list: needs to be graded by online llms (see notes)
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---
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## coverage report (trial deliverable)
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- not a data stage => reads the finished cards and summarises them
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- the trial (english + italian nouns, local models) runs steps 1a→3 then emits a report
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### pipeline health — did it run?
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- input lemmas → output cards (ratio; multi-sense makes cards > lemmas)
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- dropped per stage + why: 2a misses (junk/inflection/gap), 2b no-gloss drops, 3 no-consensus exclusions
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- parse failures / errors
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### coverage — how complete?
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- translation completeness: all 4 langs vs gaps, per language
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- examples: from Kaikki vs LLM-generated vs none
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- gender: translations still null after Wiktionary fill
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- difficulty distribution per bucket (lopsided = broken CEFRLex threshold or bad grading)
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### local good enough? — model agreement
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- difficulty votes: 3-0 / 2-1 / 3-way split rates (high 3-way = locals can't do it → need cloud)
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- translation gap-fill: agreement rate, gaps left unfilled
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- → go/no-go on local-for-launch
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### rent vs API? — workload volume
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- total LLM calls across 2c + 3 (× per-token rate = API cost)
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- cards needing LLM vs handled deterministically (english: high deterministic; italian: ~0, no CEFRLex)
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- per-language call counts → scale english (low) and italian (high) to estimate the middle three
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### review backlog — what's deferred
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- miss-list size + composition, per language
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- no-consensus exclusions (cloud-audit queue size)
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- unfilled translation gaps (also cloud-audit work)
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**framing**: english = optimistic floor (rich Kaikki, CEFRLex exists, models strongest).
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italian = pessimistic ceiling (no CEFRLex, thinner Kaikki). all five languages sit between.
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read to decide, not to archive.
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---
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Notes:
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- the miss list needs to get verified/re-worked later on
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- the kaikki data contains ipas and links to audio files, add them later if needed, they are not needed now
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- use online llms to set the difficulty(cefr) of the not shipped words
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- add plurals from kaikki/wiktionary
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- postgres sync to prod db
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@ -1,41 +0,0 @@
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# trial run — implementation roadmap (pure vertical)
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goal: english + italian nouns through the full pipeline on local models, emit
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coverage report. build a thin end-to-end slice first, then widen each pass.
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## phase 0 — foundation
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- confirm @lila/shared types (lang codes, POS) exist and match
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- package.json deps for what phase 1 needs
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- one hardcoded test word to carry through the slice (e.g. "house")
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## phase 1 — thin vertical slice (ONE word, 1a→3, crudest possible)
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goal: prove a single card can travel the whole pipeline and come out the end.
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allowed to be ugly — hardcode, skip voting, one model, fake CEFRLex.
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- 1a: one lemma record, by hand or trivial read
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- 2a: look it up in Kaikki, confirm it exists
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- 2b: extract its senses → card(s) with gloss
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- 2c: fill one missing translation with ONE local model, no voting
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- 3: assign a difficulty crudely (even hardcoded "easy")
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- OUT: one finished card. the pipeline has a shape.
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## phase 2 — widen: real deterministic front (1a–2b, all words)
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- 1a: real frequency list, verified
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- 2a: real existence gate + miss-list triage
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- 2b: real sense extraction, gender from tags, gloss-required drop
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- still JSON output. inspect cards by hand.
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## phase 3 — widen: real LLM back (2c + 3)
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- storage seam: JSON → SQLite, schema, resumability
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- 2c: real generate→vote, three families, all sub-passes
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- 3: real CEFRLex single-sense + 3-model vote multi-sense + exclude-on-split
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## phase 4 — coverage report + runs
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- emit report
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- run english, then italian
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- decide: local good enough? rent vs API?
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