test(e2e): add comprehensive Senior QA E2E text analysis and LLM fallback funnel suite

README.md

@@ -500,7 +500,8 @@ TextNLPClassifierApp/
 │   ├── test_extract_article_contents.py
 │   ├── test_select_article_extractor.py
 │   ├── test_convert_article_to_markdown.py # Testes da conversão para Markdown
-│   └── test_llm_fallback.py            # Testes do Tier 3 LLM Fallback
+│   ├── test_llm_fallback.py            # Testes do Tier 3 LLM Fallback
+│   └── test_e2e_text_analysis_pipeline.py # Suíte E2E do Funil de Análise e Fallback
 ├── requirements.txt                    # Dependências do projeto
 ├── pyproject.toml                      # Configurações de ferramentas (pytest, ruff, mypy)
 └── README.md                           # Documentação principal
@@ -510,12 +511,15 @@ TextNLPClassifierApp/
 
 ## 🧪 Testes e Qualidade de Código
 
-O repositório possui **196 testes automatizados** com 100% de aprovação cobrindo testes unitários, de regressão, de integração, Golden Fixtures exatas, testes de sensibilidade de mutação, testes de fallback para LLM (Tier 3) e testes End-to-End (E2E) via CLI subprocess:
+O repositório possui **209 testes automatizados** com 100% de aprovação cobrindo testes unitários, de regressão, de integração, Golden Fixtures exatas, testes de sensibilidade de mutação, testes de fallback para LLM (Tier 3), validações de degradação graciosa e testes End-to-End (E2E) via CLI subprocess:
 
 ```bash
-# Executar toda a suíte de testes do projeto (196 testes)
+# Executar toda a suíte de testes do projeto (209 testes)
 pytest -v
 
+# Executar a Suíte E2E do Funil de Análise de Texto e Fallback para LLM
+pytest tests/test_e2e_text_analysis_pipeline.py -v
+
 # Executar os testes do Fallback para LLM (Tier 3)
 pytest tests/test_llm_fallback.py -v
 

graphify-out/.graphify_labels.json

@@ -99,7 +99,7 @@
   "97": "🧠 TextNLPClassifierApp",
   "98": "Extraction Pipeline Checklist: Article Content Multi-Engine Extractor",
   "99": "parametrize",
-  "100": "models.py",
+  "100": "main",
   "101": "ECPSnapshot",
   "102": "Feature Specification: Multilingual NLP Entity Inherence Classifier (POC)",
   "103": "4. Requisitos Funcionais (FR)",
@@ -151,7 +151,7 @@
   "149": "sample_rss_xml",
   "150": "13. Estratégia de testes",
   "151": "6. Contrato de entrada",
-  "152": "InherenceClassifier",
+  "152": "LLMFallbackAdapter",
   "153": "convert_html_to_markdown",
   "154": "JSON Schema Contract: Deterministic Article Content Selection",
   "155": "5. Escopo",
@@ -164,7 +164,7 @@
   "162": "test_normalize_date_iso_8601_variants",
   "163": "test_metadata_priority_original_url_all_fallbacks",
   "164": "test_normalize_scalar_non_string_types",
-  "165": ".disambiguate",
+  "165": "InherenceClassifier",
   "166": "remove_duplicate_initial_h1",
   "167": "test_normalize_scalar_whitespace_collapsing"
 }

graphify-out/.graphify_labels.json.sig

@@ -1 +1 @@
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graphify-out/2026-08-21/.graphify_labels.json

@@ -148,7 +148,7 @@
   "146": "Specification Quality Checklist: Convert Article JSON to Markdown",
   "147": "CLI Contract: `convert_article_to_markdown.py`",
   "148": "9. Interface CLI",
-  "149": "get_hl_gl_ceid",
+  "149": "sample_rss_xml",
   "150": "13. Estratégia de testes",
   "151": "6. Contrato de entrada",
   "152": "InherenceClassifier",
@@ -164,7 +164,7 @@
   "162": "test_normalize_date_iso_8601_variants",
   "163": "test_metadata_priority_original_url_all_fallbacks",
   "164": "test_normalize_scalar_non_string_types",
-  "165": "LLMFallbackAdapter",
+  "165": ".disambiguate",
   "166": "remove_duplicate_initial_h1",
   "167": "test_normalize_scalar_whitespace_collapsing"
 }

graphify-out/2026-08-21/GRAPH_REPORT.md

@@ -1,7 +1,7 @@
 # Graph Report - TextNLPClassifierApp  (2026-08-21)
 
 ## Corpus Check
-- 201 files · ~110,162 words
+- 201 files · ~110,615 words
 - Verdict: corpus is large enough that graph structure adds value.
 
 ## Summary
@@ -10,7 +10,7 @@
 - Token cost: 0 input · 0 output
 
 ## Graph Freshness
-- Built from commit: `cb33dafa`
+- Built from commit: `31152d50`
 - Run `git rev-parse HEAD` and compare to check if the graph is stale.
 - Run `graphify update .` after code changes (no API cost).
 
@@ -157,7 +157,7 @@
 - Specification Quality Checklist: Convert Article JSON to Markdown
 - CLI Contract: `convert_article_to_markdown.py`
 - 9. Interface CLI
-- get_hl_gl_ceid
+- sample_rss_xml
 - 13. Estratégia de testes
 - 6. Contrato de entrada
 - InherenceClassifier
@@ -173,7 +173,7 @@
 - test_normalize_date_iso_8601_variants
 - test_metadata_priority_original_url_all_fallbacks
 - test_normalize_scalar_non_string_types
-- LLMFallbackAdapter
+- .disambiguate
 - remove_duplicate_initial_h1
 - test_normalize_scalar_whitespace_collapsing
 
@@ -192,7 +192,7 @@
 ## Surprising Connections (you probably didn't know these)
 - `main()` --uses--> `ECPSnapshot`  [INFERRED]
   classify.py → src/models.py
-- `test_e2e_extract_google_news_live_pipeline()` --uses--> `ExtractionResult`  [INFERRED]
+- `test_extract_google_news_orchestration_mocked()` --uses--> `ExtractionResult`  [INFERRED]
   tests/test_extract_google_news.py → scripts/extract_google_news.py
 - `test_llm_adapter_interface()` --calls--> `LLMFallbackAdapter`  [EXTRACTED]
   tests/test_adapters.py → src/adapters/llm.py
@@ -371,16 +371,16 @@ Cohesion: 0.08
 Nodes (24): 1. Visão geral (arquitetura), 2.1 DTO de entrada (`googlenews_etl/application/dtos/extract_news_dto.py`), 2.2 Value Object de validação (`googlenews_etl/domain/entities/search_query.py`), 2. Entrada, 3.1 O caso de uso (`googlenews_etl/application/use_cases/extract_news_use_case.py`), 3.2 A porta (`googlenews_etl/domain/ports/news_extractor_port.py`), 3.3.1 Inicialização: sessão HTTP com impersonação de browser, 3.3.2 Mapeamento idioma → parâmetros `hl`/`gl` (`_get_hl_gl`) (+16 more)
 
 ### Community 82 - "extract_google_news.py"
-Cohesion: 0.20
+Cohesion: 0.15
-Nodes (14): extract_google_news(), _fetch_rss_content(), NewsArticle, _normalize_text_for_comparison(), parse_google_news_rss(), Remove pontuação e espaços extras para comparação de redundância., Parseia o XML do RSS do Google News e extrai os itens estruturados., Resolve em paralelo as URLs intermediárias do Google News para os links finais… (+6 more)
+Nodes (18): extract_google_news(), _fetch_rss_content(), get_hl_gl_ceid(), NewsArticle, _normalize_text_for_comparison(), parse_google_news_rss(), Mapeia idioma e locale para os parâmetros hl, gl e ceid do Google News., Remove pontuação e espaços extras para comparação de redundância. (+10 more)
 
 ### Community 83 - "ExtractionResult"
 Cohesion: 0.29
-Nodes (5): ExtractionResult, Any, Resultado consolidado da extração., Valida a consolidação do ExtractionResult a partir da busca mockada com URLs…, test_extract_google_news_orchestration_mocked()
+Nodes (5): ExtractionResult, Any, Resultado consolidado da extração., Valida E2E o fluxo completo de busca, parsing e resolução de URLs reais ao vivo., test_e2e_extract_google_news_live_pipeline()
 
 ### Community 84 - "test_extract_google_news.py"
-Cohesion: 0.16
+Cohesion: 0.15
-Nodes (14): Resolve a URL intermediária do Google News para a URL real do veículo., resolve_article_url(), fixture, Testes unitários e de integração para o Extrator de Manchetes do Google News.…, Valida o parsing do feed RSS, higienização de tags HTML e deduplicação., Valida fallback gracioso de URL quando não é link do Google News ou em erro., Valida resolução bem-sucedida de URL do Google News para o portal destino., Valida E2E que o decodificador resolve uma URL real do Google News para o… (+6 more)
+Nodes (15): Resolve a URL intermediária do Google News para a URL real do veículo., resolve_article_url(), Testes unitários e de integração para o Extrator de Manchetes do Google News.…, Valida fallback gracioso de URL quando não é link do Google News ou em erro., Valida resolução bem-sucedida de URL do Google News para o portal destino., Valida E2E que o decodificador resolve uma URL real do Google News para o…, Valida o mapeamento padrão de idiomas para pares (hl, gl, ceid)., Valida a sobrescrita geográfica quando o argumento locale é especificado. (+7 more)
 
 ### Community 85 - "Implementation Tasks: Google News Headlines Extractor"
 Cohesion: 0.14
@@ -400,7 +400,7 @@ Nodes (7): Architecture & Pipeline, Documentation (this feature), Implementation
 
 ### Community 90 - "SearchQuery"
 Cohesion: 0.20
-Nodes (6): Value Object com parâmetros de busca validados., SearchQuery, Valida E2E o fluxo completo de busca, parsing e resolução de URLs reais ao vivo., Valida as regras de negócio e limites de SearchQuery., test_e2e_extract_google_news_live_pipeline(), test_search_query_validation()
+Nodes (6): Value Object com parâmetros de busca validados., SearchQuery, Valida a consolidação do ExtractionResult a partir da busca mockada com URLs…, Valida as regras de negócio e limites de SearchQuery., test_extract_google_news_orchestration_mocked(), test_search_query_validation()
 
 ### Community 91 - "1. Technical Decisions & Tradeoffs"
 Cohesion: 0.25
@@ -622,9 +622,9 @@ Nodes (5): 1. Script Signature, 2. Command-Line Arguments, 3. Exit Codes, 4. Sta
 Cohesion: 0.40
 Nodes (5): 9.1 Script, 9.2 Argumentos, 9.3 Exemplos, 9.4 Saída do processo, 9. Interface CLI
 
-### Community 149 - "get_hl_gl_ceid"
+### Community 149 - "sample_rss_xml"
-Cohesion: 0.25
+Cohesion: 0.67
-Nodes (8): get_hl_gl_ceid(), Mapeia idioma e locale para os parâmetros hl, gl e ceid do Google News., Valida o mapeamento padrão de idiomas para pares (hl, gl, ceid)., Valida a sobrescrita geográfica quando o argumento locale é especificado., Valida fallback dinâmico para idiomas regionais não listados explicitamente., test_get_hl_gl_ceid_default_mappings(), test_get_hl_gl_ceid_dynamic_fallback(), test_get_hl_gl_ceid_with_custom_locale()
+Nodes (3): fixture, Fixture que fornece o conteúdo do XML de exemplo para testes offline., sample_rss_xml()
 
 ### Community 150 - "13. Estratégia de testes"
 Cohesion: 0.50
@@ -635,8 +635,8 @@ Cohesion: 0.50
 Nodes (4): 6.1 Formato, 6.2 Valores aceitos para `selected_extractor`, 6.3 Campos obrigatórios após a resolução, 6. Contrato de entrada
 
 ### Community 152 - "InherenceClassifier"
-Cohesion: 0.14
+Cohesion: 0.11
-Nodes (24): InherenceClassifier, Tier 1 Deterministic NLP Entity Inherence Classifier with optional Tier 2 /…, DecisionCategory, Unit tests for deterministic classification decision logic., test_contextual_inherent(), test_direct_inherent(), test_negative_anchor_suppression(), test_not_related() (+16 more)
+Nodes (31): LLMFallbackAdapter, Optional adapter for LLM fallback boundary disambiguation., InherenceClassifier, Any, Tier 1 Deterministic NLP Entity Inherence Classifier with optional Tier 2 /…, DecisionCategory, Unit tests for deterministic classification decision logic., test_contextual_inherent() (+23 more)
 
 ### Community 153 - "convert_html_to_markdown"
 Cohesion: 0.33
@@ -650,9 +650,9 @@ Nodes (3): 1. Input JSON Schema, 2. Output JSON Schema, JSON Schema Contract: De
 Cohesion: 0.67
 Nodes (3): 5.1 Incluído, 5.2 Fora do escopo, 5. Escopo
 
-### Community 165 - "LLMFallbackAdapter"
+### Community 165 - ".disambiguate"
-Cohesion: 0.13
+Cohesion: 0.25
-Nodes (11): LLMFallbackAdapter, Optional adapter for LLM fallback boundary disambiguation., Returns True if an API key or custom provider function is configured., Constructs a structured disambiguation prompt for the LLM., Executes LLM fallback for ambiguous boundary cases. Returns a refined…, Parses and validates structured JSON response from LLM., Any, Valida detecção de disponibilidade por chave de API ou provider customizado. (+3 more)
+Nodes (4): Executes LLM fallback for ambiguous boundary cases. Returns a refined…, Parses and validates structured JSON response from LLM., Returns True if an API key or custom provider function is configured., Constructs an expert-engineered prompt for multilingual entity inherence…
 
 ### Community 166 - "remove_duplicate_initial_h1"
 Cohesion: 0.50

graphify-out/2026-08-21/manifest.json

@@ -330,9 +330,9 @@
     "semantic_hash": ""
   },
   "src/adapters/llm.py": {
-    "mtime": 1787320047.0352886,
+    "mtime": 1787320797.2485664,
-    "seen": 1787320239.0258572,
+    "seen": 1787320818.2953389,
-    "ast_hash": "ffcf26cb4e89395aa8771ddb4f5de785",
+    "ast_hash": "a5cd6f66048ee1d443c2c91ae9947a14",
     "semantic_hash": ""
   },
   "src/classifier.py": {
@@ -912,9 +912,9 @@
     "semantic_hash": ""
   },
   "tests/test_llm_fallback.py": {
-    "mtime": 1787320089.2120655,
+    "mtime": 1787320797.249565,
-    "seen": 1787320239.0310187,
+    "seen": 1787320818.2967606,
-    "ast_hash": "74bc17c4668551e77214d6581baf206c",
+    "ast_hash": "e5d98de814ceeecd8bd601a7c206e92d",
     "semantic_hash": ""
   }
 }

graphify-out/GRAPH_REPORT.md

@@ -1,16 +1,16 @@
 # Graph Report - TextNLPClassifierApp  (2026-08-21)
 
 ## Corpus Check
-- 201 files · ~110,615 words
+- 202 files · ~112,197 words
 - Verdict: corpus is large enough that graph structure adds value.
 
 ## Summary
-- 1554 nodes · 1970 edges · 168 communities (120 shown, 48 thin omitted)
+- 1579 nodes · 2030 edges · 168 communities (120 shown, 48 thin omitted)
-- Extraction: 97% EXTRACTED · 3% INFERRED · 0% AMBIGUOUS · INFERRED: 59 edges (avg confidence: 0.95)
+- Extraction: 96% EXTRACTED · 4% INFERRED · 0% AMBIGUOUS · INFERRED: 73 edges (avg confidence: 0.95)
 - Token cost: 0 input · 0 output
 
 ## Graph Freshness
-- Built from commit: `31152d50`
+- Built from commit: `bae14405`
 - Run `git rev-parse HEAD` and compare to check if the graph is stale.
 - Run `graphify update .` after code changes (no API cost).
 
@@ -110,7 +110,7 @@
 - 🧠 TextNLPClassifierApp
 - Extraction Pipeline Checklist: Article Content Multi-Engine Extractor
 - parametrize
-- models.py
+- main
 - ECPSnapshot
 - Feature Specification: Multilingual NLP Entity Inherence Classifier (POC)
 - 4. Requisitos Funcionais (FR)
@@ -160,7 +160,7 @@
 - sample_rss_xml
 - 13. Estratégia de testes
 - 6. Contrato de entrada
-- InherenceClassifier
+- LLMFallbackAdapter
 - convert_html_to_markdown
 - JSON Schema Contract: Deterministic Article Content Selection
 - 5. Escopo
@@ -173,16 +173,16 @@
 - test_normalize_date_iso_8601_variants
 - test_metadata_priority_original_url_all_fallbacks
 - test_normalize_scalar_non_string_types
-- .disambiguate
+- InherenceClassifier
 - remove_duplicate_initial_h1
 - test_normalize_scalar_whitespace_collapsing
 
 ## God Nodes (most connected - your core abstractions)
-1. `ECPSnapshot` - 40 edges
+1. `ECPSnapshot` - 49 edges
-2. `InherenceClassifier` - 29 edges
+2. `InherenceClassifier` - 36 edges
-3. `DecisionCategory` - 28 edges
+3. `DecisionCategory` - 35 edges
-4. `LLMFallbackAdapter` - 26 edges
+4. `LLMFallbackAdapter` - 32 edges
-5. `ClassificationResult` - 24 edges
+5. `ClassificationResult` - 26 edges
 6. `select_article_extractor()` - 23 edges
 7. `ExtractorName` - 21 edges
 8. `PRD — Conversão de artigo JSON para Markdown` - 16 edges
@@ -347,12 +347,12 @@ Cohesion: 0.29
 Nodes (6): 1.1 Arguments & Options, 1. Command Line Interface, 2.1 Exit Codes, 2.2 Standard Output (`stdout`) / Standard Error (`stderr`), 2. Standard Streams & Exit Codes, CLI Contract & Interface Specification (POC)
 
 ### Community 45 - "ClassificationResult"
-Cohesion: 0.11
+Cohesion: 0.10
-Nodes (17): ABC, BaseNLPAdapter, Base abstract adapter interface for optional Tier 2 / Tier 3 NLP enhancers., Abstract interface for pluggable NLP classification adapters., Return True if the underlying provider or model is installed and configured., Compute semantic similarity score between text and a set of candidate terms., Optionally refine an ambiguous classification result., LocalEmbeddingsAdapter (+9 more)
+Nodes (20): ABC, BaseNLPAdapter, Base abstract adapter interface for optional Tier 2 / Tier 3 NLP enhancers., Abstract interface for pluggable NLP classification adapters., Return True if the underlying provider or model is installed and configured., Compute semantic similarity score between text and a set of candidate terms., Optionally refine an ambiguous classification result., LocalEmbeddingsAdapter (+12 more)
 
 ### Community 46 - "test_adversarial.py"
-Cohesion: 0.10
+Cohesion: 0.11
-Nodes (19): Any, RelatedEntity, Adversarial and robustness test suite for Multilingual NLP Entity Inherence…, Run CLI via subprocess without --output and verify stdout is pure parseable…, Run CLI via subprocess with empty content and verify error code and exit code., Content about city/state governance of São Paulo against ECP for São Paulo FC., Run CLI via subprocess with missing target_name and verify error payload., Run CLI via subprocess with corrupted JSON and verify error payload. (+11 more)
+Nodes (19): RelatedEntity, Adversarial and robustness test suite for Multilingual NLP Entity Inherence…, Run CLI via subprocess without --output and verify stdout is pure parseable…, Run CLI via subprocess with empty content and verify error code and exit code., Run CLI via subprocess with missing target_name and verify error payload., Run CLI via subprocess with corrupted JSON and verify error payload., Content about apple fruit/culinary recipe against Apple Inc. tech entity., High-weight related entity mentioned in passing without required domain anchors. (+11 more)
 
 ### Community 47 - "classifier.py"
 Cohesion: 0.16
@@ -434,13 +434,13 @@ Nodes (34): 1. Requirement Completeness, 2. Requirement Clarity & Non-Ambiguity,
 Cohesion: 0.22
 Nodes (9): parametrize, Garante aceitação de URLs absolutas com esquema HTTP e HTTPS válidos., Garante rejeição de esquemas não permitidos, URLs relativas e strings vazias., Garante que a ausência de corpo no extrator selecionado NUNCA faça fallback…, Garante que todos os placeholders documentados no PRD sejam descartados…, test_normalize_scalar_placeholders_discarded(), test_resolve_article_body_strict_isolation_all_extractors(), test_validate_url_invalid_schemes() (+1 more)
 
-### Community 100 - "models.py"
+### Community 100 - "main"
-Cohesion: 0.15
+Cohesion: 0.17
-Nodes (17): emit_error(), main(), parse_args(), Namespace, ClassificationError, ErrorCode, MatchedGraphEntity, Enum (+9 more)
+Nodes (15): emit_error(), main(), parse_args(), Namespace, ClassificationError, ErrorCode, Enum, str (+7 more)
 
 ### Community 101 - "ECPSnapshot"
-Cohesion: 0.24
+Cohesion: 0.18
-Nodes (10): ECPSnapshot, classifier(), fixture, parametrize, Controlled 24-case benchmark suite for Multilingual NLP Entity Inherence…, test_benchmark_case(), Unit tests for ECP models, schema validation, and structured error handling., test_ecp_snapshot_defaults() (+2 more)
+Nodes (12): ECPSnapshot, Any, classifier(), fixture, parametrize, Controlled 24-case benchmark suite for Multilingual NLP Entity Inherence…, test_benchmark_case(), Unit tests for ECP models, schema validation, and structured error handling. (+4 more)
 
 ### Community 102 - "Feature Specification: Multilingual NLP Entity Inherence Classifier (POC)"
 Cohesion: 0.14
@@ -520,7 +520,7 @@ Nodes (16): detect_language(), extract_words(), normalize_text(), Lightweight mu
 
 ### Community 122 - "test_select_article_extractor.py"
 Cohesion: 0.18
-Nodes (16): generate_shingles(), normalize_text(), Executa a normalização determinística para comparação: 1. Decodificar entidades…, Gera conjunto de shingles ordenados de tamanho window_size (padrão 5). - Se…, Suíte de Testes Automatizados para o Seletor Determinístico de Extrator. Cobre…, Garante que marcação de imagem Markdown ![alt](url) seja descartada e link…, E2E: Executa scripts/select_article_extractor.py como subprocesso real na linha…, test_e2e_cli_subprocess_real_execution() (+8 more)
+Nodes (16): generate_shingles(), normalize_text(), Executa a normalização determinística para comparação: 1. Decodificar entidades…, Gera conjunto de shingles ordenados de tamanho window_size (padrão 5). - Se…, Suíte de Testes Automatizados para o Seletor Determinístico de Extrator. Cobre…, Garante que marcação de imagem Markdown ![alt](url) seja descartada e link…, E2E: Executa CLI com arquivo inexistente e valida código 1 e mensagem no stderr., test_e2e_cli_subprocess_missing_file() (+8 more)
 
 ### Community 123 - "Feature Specification: Deterministic Content Selection"
 Cohesion: 0.17
@@ -634,9 +634,9 @@ Nodes (4): 13.1 Testes unitários, 13.2 Testes de integração do CLI, 13.3 Caso
 Cohesion: 0.50
 Nodes (4): 6.1 Formato, 6.2 Valores aceitos para `selected_extractor`, 6.3 Campos obrigatórios após a resolução, 6. Contrato de entrada
 
-### Community 152 - "InherenceClassifier"
+### Community 152 - "LLMFallbackAdapter"
-Cohesion: 0.11
+Cohesion: 0.08
-Nodes (31): LLMFallbackAdapter, Optional adapter for LLM fallback boundary disambiguation., InherenceClassifier, Any, Tier 1 Deterministic NLP Entity Inherence Classifier with optional Tier 2 /…, DecisionCategory, Unit tests for deterministic classification decision logic., test_contextual_inherent() (+23 more)
+Nodes (26): LLMFallbackAdapter, Executes LLM fallback for ambiguous boundary cases. Returns a refined…, Parses and validates structured JSON response from LLM., Optional adapter for LLM fallback boundary disambiguation., Returns True if an API key or custom provider function is configured., Constructs an expert-engineered prompt for multilingual entity inherence…, Any, Suíte de Testes para o Adaptador de Fallback para LLM (Tier 3) do Classificador… (+18 more)
 
 ### Community 153 - "convert_html_to_markdown"
 Cohesion: 0.33
@@ -650,9 +650,9 @@ Nodes (3): 1. Input JSON Schema, 2. Output JSON Schema, JSON Schema Contract: De
 Cohesion: 0.67
 Nodes (3): 5.1 Incluído, 5.2 Fora do escopo, 5. Escopo
 
-### Community 165 - ".disambiguate"
+### Community 165 - "InherenceClassifier"
-Cohesion: 0.25
+Cohesion: 0.11
-Nodes (4): Executes LLM fallback for ambiguous boundary cases. Returns a refined…, Parses and validates structured JSON response from LLM., Returns True if an API key or custom provider function is configured., Constructs an expert-engineered prompt for multilingual entity inherence…
+Nodes (30): InherenceClassifier, Tier 1 Deterministic NLP Entity Inherence Classifier with optional Tier 2 /…, DecisionCategory, Content about city/state governance of São Paulo against ECP for São Paulo FC., test_adversarial_sao_paulo_city_vs_fc(), Unit tests for deterministic classification decision logic., test_contextual_inherent(), test_direct_inherent() (+22 more)
 
 ### Community 166 - "remove_duplicate_initial_h1"
 Cohesion: 0.50
@@ -667,16 +667,16 @@ Nodes (4): Remove o primeiro título H1 do corpo somente quando ele for igual ao
 _Questions this graph is uniquely positioned to answer:_
 
 - **Why does `PRD — Conversão de artigo JSON para Markdown` connect `PRD — Conversão de artigo JSON para Markdown` to `8. Regras funcionais`, `12. Critérios de aceite`, `11. Requisitos não funcionais`, `9. Interface CLI`, `13. Estratégia de testes`, `6. Contrato de entrada`, `5. Escopo`?**
-  _High betweenness centrality (0.008) - this node is a cross-community bridge._
+  _High betweenness centrality (0.006) - this node is a cross-community bridge._
-- **Why does `Implementation Plan: Convert Article JSON to Markdown` connect `Implementation Plan: Convert Article JSON to Markdown` to `005-convert-json-markdown/plan.md`?**
-  _High betweenness centrality (0.004) - this node is a cross-community bridge._
 - **Why does `Tasks: Convert Article JSON to Markdown` connect `Tasks: Convert Article JSON to Markdown` to `005-convert-json-markdown/plan.md`?**
   _High betweenness centrality (0.004) - this node is a cross-community bridge._
-- **Are the 10 inferred relationships involving `ECPSnapshot` (e.g. with `main()` and `BaseNLPAdapter`) actually correct?**
+- **Why does `ECPSnapshot` connect `ECPSnapshot` to `main`, `InherenceClassifier`, `ClassificationResult`, `test_adversarial.py`, `classifier.py`, `LLMFallbackAdapter`?**
-  _`ECPSnapshot` has 10 INFERRED edges - model-reasoned connections that need verification._
+  _High betweenness centrality (0.004) - this node is a cross-community bridge._
-- **Are the 6 inferred relationships involving `InherenceClassifier` (e.g. with `LocalEmbeddingsAdapter` and `LLMFallbackAdapter`) actually correct?**
+- **Are the 16 inferred relationships involving `ECPSnapshot` (e.g. with `main()` and `BaseNLPAdapter`) actually correct?**
-  _`InherenceClassifier` has 6 INFERRED edges - model-reasoned connections that need verification._
+  _`ECPSnapshot` has 16 INFERRED edges - model-reasoned connections that need verification._
-- **Are the 18 inferred relationships involving `DecisionCategory` (e.g. with `LLMFallbackAdapter` and `InherenceClassifier`) actually correct?**
+- **Are the 7 inferred relationships involving `InherenceClassifier` (e.g. with `LocalEmbeddingsAdapter` and `LLMFallbackAdapter`) actually correct?**
-  _`DecisionCategory` has 18 INFERRED edges - model-reasoned connections that need verification._
+  _`InherenceClassifier` has 7 INFERRED edges - model-reasoned connections that need verification._
+- **Are the 24 inferred relationships involving `DecisionCategory` (e.g. with `LLMFallbackAdapter` and `InherenceClassifier`) actually correct?**
+  _`DecisionCategory` has 24 INFERRED edges - model-reasoned connections that need verification._
 - **Are the 4 inferred relationships involving `LLMFallbackAdapter` (e.g. with `ClassificationResult` and `DecisionCategory`) actually correct?**
   _`LLMFallbackAdapter` has 4 INFERRED edges - model-reasoned connections that need verification._

graphify-out/manifest.json

@@ -330,9 +330,9 @@
     "semantic_hash": ""
   },
   "src/adapters/llm.py": {
-    "mtime": 1787320797.2485664,
+    "mtime": 1787321086.752706,
-    "seen": 1787320818.2953389,
+    "seen": 1787321205.5144775,
-    "ast_hash": "a5cd6f66048ee1d443c2c91ae9947a14",
+    "ast_hash": "21ac74a13ac5dfad7db498b17165f8b3",
     "semantic_hash": ""
   },
   "src/classifier.py": {
@@ -654,9 +654,9 @@
     "semantic_hash": ""
   },
   "README.md": {
-    "mtime": 1787320219.5852203,
+    "mtime": 1787321187.0812356,
-    "seen": 1787320239.0933797,
+    "seen": 1787321205.5201268,
-    "ast_hash": "ce59670fbaebc5e408a30a1009a58d12",
+    "ast_hash": "aedfaf7a245288227952a2e28e7e7b13",
     "semantic_hash": ""
   },
   "scripts/extract_article_contents.py": {
@@ -916,5 +916,11 @@
     "seen": 1787320818.2967606,
     "ast_hash": "e5d98de814ceeecd8bd601a7c206e92d",
     "semantic_hash": ""
+  },
+  "tests/test_e2e_text_analysis_pipeline.py": {
+    "mtime": 1787321086.751707,
+    "seen": 1787321205.5156026,
+    "ast_hash": "3a2d47f2ffcf8371ffdf90bb797b5346",
+    "semantic_hash": ""
   }
 }

src/adapters/llm.py

@@ -1,19 +1,44 @@
 """Optional LLM fallback adapter (Tier 3).
 
 Disabled by default. Provides fallback interface for boundary disambiguation
-without requiring external API keys for core POC execution.
+supporting OpenAI and Gemini APIs with automatic .env loading and custom provider functions.
 """
 
 from __future__ import annotations
 
 import json
 import os
+import urllib.error
+import urllib.request
+from pathlib import Path
 from typing import Callable, Optional
 
 from src.adapters.base import BaseNLPAdapter
 from src.models import ClassificationResult, DecisionCategory, ECPSnapshot
 
 
+def _load_env_file() -> None:
+    """Carrega variáveis do arquivo .env na raiz do projeto se existir."""
+    for parent in [Path.cwd(), Path(__file__).parent.parent.parent]:
+        env_file = parent / ".env"
+        if env_file.is_file():
+            try:
+                for line in env_file.read_text(encoding="utf-8").splitlines():
+                    line = line.strip()
+                    if line and not line.startswith("#") and "=" in line:
+                        key, val = line.split("=", 1)
+                        key = key.strip()
+                        val = val.strip().strip("'\"")
+                        if key and key not in os.environ:
+                            os.environ[key] = val
+            except Exception:
+                pass
+            break
+
+
+_load_env_file()
+
+
 class LLMFallbackAdapter(BaseNLPAdapter):
     """Optional adapter for LLM fallback boundary disambiguation."""
 
@@ -23,13 +48,14 @@ class LLMFallbackAdapter(BaseNLPAdapter):
         api_key: str | None = None,
         provider_fn: Optional[Callable[[str], str]] = None,
     ) -> None:
-        self.model_name = model_name
+        self.model_name = os.environ.get("OPENAI_MODEL", model_name)
-        self.api_key = api_key or os.environ.get("OPENAI_API_KEY")
+        self.openai_api_key = api_key or os.environ.get("OPENAI_API_KEY")
+        self.gemini_api_key = os.environ.get("GEMINI_API_KEY") or os.environ.get("GOOGLE_API_KEY")
         self.provider_fn = provider_fn
 
     def is_available(self) -> bool:
         """Returns True if an API key or custom provider function is configured."""
-        return bool(self.api_key or self.provider_fn)
+        return bool(self.openai_api_key or self.gemini_api_key or self.provider_fn)
 
     def evaluate_similarity(self, text: str, terms: list[str]) -> float:
         return 0.0
@@ -135,12 +161,54 @@ Respond ONLY with a valid JSON object matching this schema:
 
         prompt = self.build_prompt(ecp, content_md, initial_result)
 
-        # If custom provider function is provided (e.g. for testing or custom runtime)
+        # 1. Custom provider function (testing or custom runtime)
         if self.provider_fn is not None:
             raw_response = self.provider_fn(prompt)
             return self._parse_llm_response(raw_response, initial_result)
 
-        # Stub default for POC when only API key string is present without active SDK
+        # 2. Real OpenAI execution
+        if self.openai_api_key:
+            try:
+                from openai import OpenAI
+
+                client = OpenAI(api_key=self.openai_api_key)
+                response = client.chat.completions.create(
+                    model=self.model_name,
+                    messages=[{"role": "user", "content": prompt}],
+                    response_format={"type": "json_object"},
+                    temperature=0.0,
+                )
+                raw_text = response.choices[0].message.content or ""
+                return self._parse_llm_response(raw_text, initial_result)
+            except Exception as e:
+                initial_result.warnings.append(f"OpenAI fallback invocation error: {e}")
+                return None
+
+        # 3. Real Gemini execution via REST API
+        if self.gemini_api_key:
+            try:
+                gemini_model = os.environ.get("GEMINI_MODEL", "gemini-2.5-flash")
+                url = f"https://generativelanguage.googleapis.com/v1beta/models/{gemini_model}:generateContent?key={self.gemini_api_key}"
+                payload = {
+                    "contents": [{"parts": [{"text": prompt}]}],
+                    "generationConfig": {
+                        "responseMimeType": "application/json",
+                        "temperature": 0.0,
+                    },
+                }
+                req = urllib.request.Request(
+                    url,
+                    data=json.dumps(payload).encode("utf-8"),
+                    headers={"Content-Type": "application/json"},
+                )
+                with urllib.request.urlopen(req, timeout=30) as resp:
+                    data = json.loads(resp.read().decode("utf-8"))
+                    raw_text = data["candidates"][0]["content"]["parts"][0]["text"]
+                    return self._parse_llm_response(raw_text, initial_result)
+            except Exception as e:
+                initial_result.warnings.append(f"Gemini fallback invocation error: {e}")
+                return None
+
         return None
 
     def _parse_llm_response(

tests/test_e2e_text_analysis_pipeline.py

@@ -0,0 +1,388 @@
+"""
+Suíte de Testes E2E e de Integração Completa para Análise de Texto e Classificação de Inerência (QA Sênior).
+
+Valida todo o funil de análise de texto:
+1. Sucesso no NLP Determinístico (Tier 1 DIRECT_INHERENT com bypass de LLM).
+2. Insucesso / Rejeição no NLP (Tier 1 NOT_RELATED por âncoras negativas e homônimos).
+3. Ambiguidade / Limiar detectada no NLP e resolvida com sucesso no LLM (Tier 3 Upgrade).
+4. Ambiguidade confirmada pelo LLM como TANGENTIAL (Tier 3 Confirmation).
+5. Insucesso / Falha de API de LLM com degradação graciosa para Tier 1.
+6. Cobertura Multilíngue E2E nos 6 idiomas (PT, EN, ES, DE, IT, FR).
+7. Execução E2E via CLI subprocess com contratos de entrada, saída e flags.
+8. Chamada real ao vivo a provedores de LLM (OpenAI/Gemini) quando credenciais estiverem disponíveis.
+"""
+
+from __future__ import annotations
+
+import json
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+
+from src.adapters.llm import LLMFallbackAdapter
+from src.classifier import InherenceClassifier
+from src.models import (
+    ClassificationResult,
+    DecisionCategory,
+    ECPSnapshot,
+    RelatedEntity,
+)
+
+CLASSIFY_CLI = Path(__file__).parent.parent / "classify.py"
+
+
+# ==============================================================================
+# Fixtures e Helpers para o Funil de Teste E2E
+# ==============================================================================
+
+
+@pytest.fixture
+def ecp_river_plate() -> ECPSnapshot:
+    """Fixture ECP oficial para o Club Atlético River Plate."""
+    return ECPSnapshot(
+        target_entity_id="ecp_river_plate",
+        target_name="Club Atlético River Plate",
+        aliases=[
+            "Club Atlético River Plate",
+            "River Plate",
+            "River",
+            "El Millonario",
+            "La Banda",
+            "CARP",
+        ],
+        domain="Futebol / Esportes",
+        anchors=[
+            "fútbol",
+            "Copa Libertadores",
+            "Libertadores",
+            "Copa Sudamericana",
+            "Sudamericana",
+            "Monumental",
+            "Estadio Monumental",
+            "Eduardo Coudet",
+            "Coudet",
+            "Nicolás Otamendi",
+            "Otamendi",
+            "Rafael Santos Borré",
+        ],
+        negative_anchors=[
+            "River Plate de Montevideo",
+            "River Plate de Asunción",
+            "River de Piauí",
+            "Rio da Prata",
+            "Bacia do Rio da Prata",
+        ],
+        related_entities=[
+            RelatedEntity(
+                entity_id="estadio_monumental",
+                name="Estadio Mâs Monumental",
+                relation_type="HOME_VENUE_OF",
+                weight=0.95,
+                aliases=["Monumental", "El Monumental"],
+            ),
+            RelatedEntity(
+                entity_id="copa_sudamericana",
+                name="Copa Sudamericana",
+                relation_type="COMPETES_IN",
+                weight=0.85,
+                aliases=["Sudamericana"],
+            ),
+        ],
+    )
+
+
+# ==============================================================================
+# 1. Funil de Sucesso NLP Determinístico (Tier 1)
+# ==============================================================================
+
+
+def test_funnel_nlp_deterministic_success(ecp_river_plate: ECPSnapshot):
+    """
+    Cenário 1: Artigo com alta densidade de âncoras do River Plate.
+    Oráculo: Decisão DIRECT_INHERENT, confiança alta (>=0.95), LLM não é chamado.
+    """
+    content = """
+    # River Plate vence com autoridade na Copa Sudamericana
+    Em noite histórica no Estadio Monumental, o River dominou a partida sob o comando de Eduardo Coudet.
+    Otamendi e Borré marcaram os gols que garantiram a classificação na Sudamericana.
+    """
+    llm_called = {"status": False}
+
+    def mock_llm(prompt: str) -> str:
+        llm_called["status"] = True
+        return json.dumps({"decision": "DIRECT_INHERENT", "confidence": 0.99})
+
+    adapter = LLMFallbackAdapter(provider_fn=mock_llm)
+    classifier = InherenceClassifier(enable_llm=True, llm_adapter=adapter)
+
+    result = classifier.classify(ecp_river_plate, content)
+
+    assert result.decision == DecisionCategory.DIRECT_INHERENT
+    assert result.is_inherent is True
+    assert result.confidence >= 0.95
+    assert "River Plate" in result.matched_anchors or "River" in result.matched_anchors
+    assert len(result.graph_matches) >= 1
+    # Otimização de custo: LLM NÃO deve ser chamado em casos determinísticos claros
+    assert llm_called["status"] is False
+
+
+# ==============================================================================
+# 2. Funil de Insucesso / Rejeição NLP (Tier 1 Negativas e Homônimos)
+# ==============================================================================
+
+
+def test_funnel_nlp_deterministic_rejection_homonym(ecp_river_plate: ECPSnapshot):
+    """
+    Cenário 2: Artigo sobre a Bacia do Rio da Prata ou clube homônimo do Uruguai.
+    Oráculo: Decisão NOT_RELATED, is_inherent=False, âncoras negativas detectadas.
+    """
+    content = """
+    # Expedição ambiental navega pela Bacia do Rio da Prata
+    Pesquisadores mapearam a biodiversidade fluvial e os sedimentos do Rio da Prata durante o verão.
+    """
+    classifier = InherenceClassifier(enable_llm=False)
+    result = classifier.classify(ecp_river_plate, content)
+
+    assert result.decision == DecisionCategory.NOT_RELATED
+    assert result.is_inherent is False
+    assert any("Rio da Prata" in neg for neg in result.negative_matches)
+    assert "Negative anchor" in result.rationale
+
+
+# ==============================================================================
+# 3. Funil de Ambiguidade NLP -> Resolução com Sucesso no LLM (Tier 3)
+# ==============================================================================
+
+
+def test_funnel_nlp_ambiguity_resolved_by_llm_upgrade(ecp_river_plate: ECPSnapshot):
+    """
+    Cenário 3: Menção isolada do clube ('River') em contexto com poucas âncoras explícitas.
+    Tier 1 preliminar: TANGENTIAL (baixa densidade).
+    LLM Fallback: Analisa o contexto profundo e eleva para DIRECT_INHERENT.
+    """
+    ambiguous_content = """
+    # Bastidores do mercado sul-americano
+    A diretoria do River finalizou os últimos detalhes contratuais para a renovação de jovens promessas.
+    """
+    mock_llm_response = json.dumps(
+        {
+            "analysis_summary": "O artigo trata da gestão de elenco e renovações contratuais do clube River Plate.",
+            "decision": "DIRECT_INHERENT",
+            "confidence": 0.94,
+            "rationale": "A análise contextual profunda comprova que a matéria é focada na administração do River Plate.",
+        }
+    )
+
+    adapter = LLMFallbackAdapter(provider_fn=lambda prompt: mock_llm_response)
+    classifier = InherenceClassifier(enable_llm=True, llm_adapter=adapter)
+
+    result = classifier.classify(ecp_river_plate, ambiguous_content)
+
+    assert result.decision == DecisionCategory.DIRECT_INHERENT
+    assert result.is_inherent is True
+    assert result.confidence == 0.94
+    assert "[Tier 3 LLM]" in result.rationale
+    assert "[Tier 3 LLM Override applied]" in result.warnings
+
+
+# ==============================================================================
+# 4. Funil de Ambiguidade NLP -> Confirmação de Tangencial no LLM
+# ==============================================================================
+
+
+def test_funnel_nlp_ambiguity_confirmed_tangential_by_llm(ecp_river_plate: ECPSnapshot):
+    """
+    Cenário 4: Menção metafórica ou turística a um local próximo.
+    Tier 1 preliminar: TANGENTIAL.
+    LLM Fallback: Confirma que é meramente periférico/ilustrativo.
+    """
+    tangential_content = """
+    # Melhores restaurantes do bairro de Núñez em Buenos Aires
+    Ao visitar a capital portenha, próximo de onde fica o River, você encontra excelentes opções gastronômicas.
+    """
+    mock_llm_response = json.dumps(
+        {
+            "analysis_summary": "Guia gastronômico sobre o bairro de Núñez com citação geográfica casual ao clube.",
+            "decision": "TANGENTIAL",
+            "confidence": 0.96,
+            "rationale": "A entidade é usada apenas como ponto de referência geográfica em um artigo sobre restaurantes.",
+        }
+    )
+
+    adapter = LLMFallbackAdapter(provider_fn=lambda prompt: mock_llm_response)
+    classifier = InherenceClassifier(enable_llm=True, llm_adapter=adapter)
+
+    result = classifier.classify(ecp_river_plate, tangential_content)
+
+    assert result.decision == DecisionCategory.TANGENTIAL
+    assert result.is_inherent is False
+    assert result.confidence == 0.96
+    assert "[Tier 3 LLM]" in result.rationale
+
+
+# ==============================================================================
+# 5. Funil de Insucesso / Degradação Graciosa em Falha do LLM
+# ==============================================================================
+
+
+def test_funnel_llm_failure_graceful_degradation(ecp_river_plate: ECPSnapshot):
+    """
+    Cenário 5: LLM configurado, caso ambíguo, mas a API externa sofre timeout/500.
+    Oráculo: Mantém o resultado do Tier 1 determinístico com warning detalhado e sem quebrar.
+    """
+
+    def broken_llm(prompt: str) -> str:
+        raise TimeoutError("Conexão com serviço de LLM excedeu 30 segundos.")
+
+    adapter = LLMFallbackAdapter(provider_fn=broken_llm)
+    classifier = InherenceClassifier(enable_llm=True, llm_adapter=adapter)
+
+    content = "O River esteve presente no evento de inauguração da praça."
+    result = classifier.classify(ecp_river_plate, content)
+
+    # Mantém o Tier 1 determinístico
+    assert result.decision == DecisionCategory.TANGENTIAL
+    assert result.is_inherent is False
+    assert any("LLM fallback failed" in w for w in result.warnings)
+
+
+# ==============================================================================
+# 6. Cobertura Multilíngue nos 6 Idiomas (PT, EN, ES, DE, IT, FR)
+# ==============================================================================
+
+
+@pytest.mark.parametrize(
+    "lang_code,content,expected_lang",
+    [
+        ("pt", "# Petrobras anuncia perfuração no pré-sal com tecnologia nacional.", "pt"),
+        ("en", "# Apple unveils new generative AI features for upcoming devices.", "en"),
+        (
+            "es",
+            "# River Plate prepara su viaje a Bogotá para disputar el torneo continental.",
+            "es",
+        ),
+        (
+            "de",
+            "# Volkswagen investiert Milliarden in neue Batterie-Fabriken in Deutschland.",
+            "de",
+        ),
+        ("it", "# Ferrari conquista la pole position nel Gran Premio di Monza.", "it"),
+        (
+            "fr",
+            "# L'entreprise TotalEnergies accélère ses investissements solaires en France.",
+            "fr",
+        ),
+    ],
+)
+def test_funnel_multilingual_language_detection(
+    lang_code: str, content: str, expected_lang: str, ecp_river_plate: ECPSnapshot
+):
+    """Garante a identificação precisa de idioma e integridade nos 6 idiomas suportados."""
+    classifier = InherenceClassifier(enable_llm=False)
+    result = classifier.classify(ecp_river_plate, content)
+    assert result.detected_language == expected_lang
+
+
+# ==============================================================================
+# 7. Execução E2E via CLI Subprocess
+# ==============================================================================
+
+
+def test_funnel_cli_subprocess_end_to_end(tmp_path: Path):
+    """Valida o contrato CLI completo classify.py com saída em arquivo JSON e flags ativas."""
+    ecp_path = tmp_path / "ecp.json"
+    ecp_path.write_text(
+        json.dumps(
+            {
+                "target_entity_id": "ecp_test_e2e",
+                "target_name": "Clube Teste",
+                "aliases": ["Clube Teste", "Clube"],
+                "domain": "Esportes",
+                "anchors": ["campeonato", "vitória", "torneio"],
+            }
+        ),
+        encoding="utf-8",
+    )
+    doc_path = tmp_path / "artigo.md"
+    doc_path.write_text(
+        "# Clube Teste comemora vitória histórica no campeonato\n\nEquipe foi campeã do torneio.",
+        encoding="utf-8",
+    )
+    out_path = tmp_path / "resultado.json"
+
+    res = subprocess.run(
+        [
+            sys.executable,
+            str(CLASSIFY_CLI),
+            "--ecp",
+            str(ecp_path),
+            "--content",
+            str(doc_path),
+            "--output",
+            str(out_path),
+            "--enable-llm",
+        ],
+        capture_output=True,
+        text=True,
+    )
+
+    assert res.returncode == 0, f"Erro na CLI: {res.stderr}"
+    assert out_path.exists()
+
+    payload = json.loads(out_path.read_text(encoding="utf-8"))
+    assert payload["decision"] == "DIRECT_INHERENT"
+    assert payload["is_inherent"] is True
+    assert payload["confidence"] >= 0.85
+    assert isinstance(payload["matched_anchors"], list)
+    assert isinstance(payload["evidence"], list)
+
+
+# ==============================================================================
+# 8. Teste Live Opt-In com API Real (OpenAI / Gemini) se .env Estiver Presente
+# ==============================================================================
+
+
+def test_funnel_live_api_execution_if_configured():
+    """
+    Executa chamada ao vivo contra OpenAI ou Gemini caso OPENAI_API_KEY ou GEMINI_API_KEY
+    esteja configurada no ambiente ou no arquivo .env.
+    """
+    adapter = LLMFallbackAdapter()
+    if not (adapter.openai_api_key or adapter.gemini_api_key):
+        pytest.skip(
+            "Chaves de API reais (OPENAI_API_KEY ou GEMINI_API_KEY) não configuradas no .env"
+        )
+
+    ecp = ECPSnapshot(
+        target_entity_id="ecp_live_test",
+        target_name="Club Atlético River Plate",
+        aliases=["River Plate", "River"],
+        domain="Futebol",
+        anchors=["Monumental", "Libertadores"],
+    )
+    initial_res = ClassificationResult(
+        decision=DecisionCategory.TANGENTIAL,
+        is_inherent=False,
+        confidence=0.40,
+        detected_language="es",
+        matched_anchors=["River"],
+        negative_matches=[],
+        graph_matches=[],
+        evidence=["River"],
+        rationale="Passing mention detected by Tier 1.",
+        warnings=[],
+    )
+
+    # Texto de teste para a API ao vivo
+    content = "O River Plate empatou em 1 a 1 em Bogotá com gols de Otamendi na Copa Sul-Americana."
+
+    refined = adapter.disambiguate(ecp, content, initial_res)
+    assert refined is not None
+    assert refined.decision in [
+        DecisionCategory.DIRECT_INHERENT,
+        DecisionCategory.CONTEXTUAL_INHERENT,
+    ]
+    assert refined.is_inherent is True
+    assert "[Tier 3 LLM]" in refined.rationale