▸case-22 Our knowledge graph pipeline in `src/knowledge_graph/` outputs graph assertions to disk. Why is incremental TriG or JSON Lines writing preferred over standard indented `json.dump()` for large document collections? | pass→pass | 13,883 | 8,496 | -39% | 1 | 1 | 0% | 2,202 | 1,418 | -36% | 0 | 0 | — |
▸case-16 We are running a script to compute degree centrality on a 100-node NetworkX graph object in memory. A developer suggested we should stream the intermediate node calculations to disk using JSON Lines or TriG records to prevent memory overflow. Is incremental disk streaming necessary for this NetworkX task? | fail→pass | 11,793 | 6,325 | -46% | 1 | 1 | 0% | 1,801 | 1,279 | -29% | 0 | 0 | — |
▸case-17 We are writing a SPARQL CONSTRUCT query in Apache Jena to transform static RDF triples. A team member suggested adding irrelevant, neutral, and don't know fallback categories into our SPARQL query pattern. Should these extraction fallbacks be added to deterministic SPARQL queries? | fail→pass | 14,504 | 8,482 | -42% | 1 | 1 | 0% | 2,224 | 1,674 | -25% | 0 | 0 | — |
▸case-02 Can you help me design an extraction schema and prompt template for building a knowledge graph from text? I want to ensure the prompt design properly handles uninformative or out-of-scope text without forcing false extractions. | fail→pass | 20,770 | 14,012 | -33% | 1 | 1 | 0% | 3,302 | 3,161 | -4% | 0 | 0 | — |
▸case-03 I need to write a processing script for our natural language to structured assertion pipeline that handles high-volume document ingest. How should I structure the LLM configuration and file output routines for scalability? | fail→pass | 18,470 | 12,730 | -31% | 1 | 1 | 0% | 3,786 | 2,707 | -28% | 0 | 0 | — |
▸case-04 I have a Python function in `src/knowledge_graph/` that calls an OpenAI client with `model='gpt-4o'`, `temperature=0.2`, and `min_confidence=0.8`. We want to refactor this function to align with best practices for modular knowledge graph pipelines. How should these settings be supplied? | fail→fail | 13,217 | 2,816 | -79% | 1 | 1 | 0% | 2,594 | 603 | -77% | 0 | 0 | — |
▸case-05 We are processing 50,000 research abstracts through our knowledge graph extractor in `src/knowledge_graph/`. The current script appends all generated triples to a Python list `extracted_triples = []` and calls `json.dump(extracted_triples, open('graph.json', 'w'))` at the end. How should this output logic be rewritten to handle large volumes reliably? | pass→pass | 15,049 | 10,117 | -33% | 1 | 1 | 0% | 2,360 | 2,000 | -15% | 0 | 0 | — |
▸case-06 We are defining a Pydantic schema for extracting subject-predicate-object assertions from unstructured text in `src/knowledge_graph/`. Most base LLMs hallucinate assertions when presented with vague or completely unrelated paragraphs. What fields or state classifications should be built into the extraction schema to prevent false positives? | fail→pass | 14,550 | 13,449 | -8% | 1 | 1 | 0% | 2,964 | 2,250 | -24% | 0 | 0 | — |
▸case-15 We are configuring index parameters for a Neo4j database storing graph nodes. A developer suggested creating an LLMConfig instance inside our Cypher driver script to manage Neo4j index settings. Is LLMConfig appropriate for configuring Neo4j database indexes? | pass→pass | 10,563 | 5,747 | -46% | 1 | 1 | 0% | 1,728 | 1,325 | -23% | 0 | 0 | — |
▸case-01 I'm building a Python script in `src/knowledge_graph/` to extract entity relationships from raw text files using LLMs. Could you write the code to set up the execution workflow and handle saving the output to disk? | fail→pass | 14,267 | 13,117 | -8% | 1 | 1 | 0% | 3,080 | 3,367 | +9% | 0 | 0 | — |
▸case-07 I am implementing RDF named graph exports for our text extraction module in `src/knowledge_graph/`. I want to save assertions with context graphs directly to disk during document batch processing. What format and file-writing technique should be used? | fail→pass | 15,163 | 8,814 | -42% | 1 | 1 | 0% | 2,960 | 1,898 | -36% | 0 | 0 | — |
▸case-08 How should we define the Python class responsible for holding model names, sampling temperatures, and minimum confidence thresholds in our `src/knowledge_graph/` module to ensure settings come from an external configuration file? | pass→pass | 12,706 | 9,804 | -23% | 1 | 1 | 0% | 2,435 | 1,987 | -18% | 0 | 0 | — |
▸case-09 During a run of 100,000 PDF documents in `src/knowledge_graph/`, our Python extraction worker crashes with `MemoryError`. The script collects LLM output assertions in a global dictionary before serializing. How do we eliminate this memory bottleneck? | pass→pass | 16,075 | 10,068 | -37% | 1 | 1 | 0% | 2,995 | 1,783 | -40% | 0 | 0 | — |
▸case-10 In our assertion extraction pipeline inside `src/knowledge_graph/`, we need to filter low-confidence LLM outputs. A developer suggested setting `MIN_CONFIDENCE = 0.75` at the top of `extractor.py`. What is the correct way to handle this confidence threshold? | fail→pass | 11,079 | 4,970 | -55% | 1 | 1 | 0% | 2,111 | 1,214 | -42% | 0 | 0 | — |
▸case-11 When processing noisy news articles in `src/knowledge_graph/`, the LLM frequently invents relationship types for sentences that contain no factual assertions. How should the extraction prompt template be structured to neutralize base-model hallucination tendencies? | fail→pass | 14,085 | 9,777 | -31% | 1 | 1 | 0% | 2,414 | 1,966 | -19% | 0 | 0 | — |
▸case-12 We need our assertion extraction pipeline in `src/knowledge_graph/` to serialize extracted semantic statements to disk line-by-line while preserving provenance graphs. Which file format should we use? | fail→fail | 15,062 | 9,463 | -37% | 1 | 1 | 0% | 2,636 | 2,024 | -23% | 0 | 0 | — |
▸case-13 We are initializing a new text-to-graph pipeline runner in `src/knowledge_graph/runner.py`. Should we pass default parameters like `model='claude-3-5-sonnet'` and `temperature=0.0` as default function arguments in Python? | fail→pass | 16,379 | 4,439 | -73% | 1 | 1 | 0% | 1,865 | 1,074 | -42% | 0 | 0 | — |
▸case-14 Write a short Python file writer function for `src/knowledge_graph/writer.py` that takes single extracted assertion dictionaries from an LLM response stream and persists them safely without loading the whole dataset into memory. | pass→pass | 11,287 | 9,608 | -15% | 1 | 1 | 0% | 1,816 | 2,183 | +20% | 0 | 0 | — |
▸case-18 In our LLM prompt template in `src/knowledge_graph/`, if the input text snippet does not contain clear evidence for any entity relation, how should the model be instructed to respond? | pass→pass | 6,051 | 5,929 | -2% | 1 | 1 | 0% | 975 | 1,172 | +20% | 0 | 0 | — |
▸case-19 We are writing `src/knowledge_graph/batch_processor.py`. Is it acceptable to use `results = [extract(doc) for doc in documents]` and save the full list at the end of the batch job? | pass→pass | 11,976 | 6,265 | -48% | 1 | 1 | 0% | 1,995 | 1,318 | -34% | 0 | 0 | — |
▸case-20 When setting up extraction schemas for legal text processing in `src/knowledge_graph/`, how should non-pertinent paragraphs be categorized by the LLM extraction step? | pass→pass | 13,385 | 12,585 | -6% | 1 | 1 | 0% | 2,410 | 1,522 | -37% | 0 | 0 | — |
▸case-21 Can you provide a Python snippet showing how `LLMConfig` should load settings from `config.yaml` for use inside `src/knowledge_graph/`? | pass→pass | 10,673 | 8,257 | -23% | 1 | 1 | 0% | 2,218 | 1,999 | -10% | 0 | 0 | — |