--- title: Automating Knowledge Graph Population: Extracting... description: A hands-on walkthrough shows how to build an automated pipeline that extracts structured entities and SPOC (Subject-Predicate-Object-Context) quads from... canonical: https://daily.dev/posts/automating-knowledge-graph-population-extracting-entities-and-triples-from-unstructured-text-with-a-occ2gvqzy twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Automating Knowledge Graph Population: Extracting Entities and Triples from Unstructured Text with an LLM | daily.dev og:description: A hands-on walkthrough shows how to build an automated pipeline that extracts structured entities and SPOC (Subject-Predicate-Object-Context) quads from... og:url: https://daily.dev/posts/automating-knowledge-graph-population-extracting-entities-and-triples-from-unstructured-text-with-a-occ2gvqzy og:image: https://api.daily.dev/og/posts/oCc2gVQZY.png og:image:alt: Automating Knowledge Graph Population: Extracting Entities and Triples from Unstructured Text with an LLM og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Automating Knowledge Graph Population: Extracting Entities and Triples from Unstructured Text with an LLM **[Machine Learning Mastery](https://daily.dev/sources/mlm)** · 4 min read · 1 upvotes · 0 comments ## Summary A hands-on walkthrough shows how to build an automated pipeline that extracts structured entities and SPOC (Subject-Predicate-Object-Context) quads from unstructured text, such as Wikipedia articles, using a locally-run Llama 3.2 model via Ollama. The pipeline uses few-shot prompting and JSON output mode to reliably parse facts, then loads the resulting quads into a QuadStore knowledge graph. This follows up on a prior piece about a deterministic, graph-based RAG architecture, closing the loop by showing where the factual graph data comes from, aiming to reduce LLM hallucinations in retrieval pipelines. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How can I extract structured triples or quads from unstructured text using a local LLM instead of a paid API? Run Llama 3.2 locally through Ollama and prompt it with few-shot examples in JSON output mode to extract Subject-Predicate-Object-Context (SPOC) quads from raw text such as Wikipedia articles. The extra context field tracks where a fact came from, letting you verify provenance before loading facts into a knowledge graph like QuadStore for retrieval. _daily.dev surfaces practical walkthroughs like this for engineers wiring local LLMs into knowledge graph pipelines._ ### What is a SPOC quad and how does it differ from a standard RDF triple? A SPOC quad extends the classic RDF triple of Subject, Predicate, Object by adding a fourth Context element, for example turning ("LeBron James", "plays_for", "Lakers") into ("LeBron James", "plays_for", "Lakers", "NBA_2023_Roster"). The context dimension records where a fact came from and helps assess whether it is still true. _Developers designing knowledge graph schemas can find deeper RAG architecture explainers through daily.dev._ ## Similar posts on daily.dev - [llm-wiki](https://daily.dev/posts/llm-wiki-7o1bskg72) · Hacker News · 7 upvotes · 0 comments - [Automating Web Research](https://daily.dev/posts/automating-web-research-pwuhcbqf7) · LangChain · 0 upvotes · 0 comments - [Making LLMs boring: From chatbots to semantic processors](https://daily.dev/posts/making-llms-boring-from-chatbots-to-semantic-processors-9trmmy1ca) · Red Hat Developer · 1 upvotes · 0 comments - [How to Build an Automatic Knowledge Graph for Your Blog with PHP and JSON-LD](https://daily.dev/posts/how-to-build-an-automatic-knowledge-graph-for-your-blog-with-php-and-json-ld-f7ssc97ol) · freeCodeCamp · 1 upvotes · 0 comments - [The feature we were afraid to talk about](https://daily.dev/posts/the-feature-we-were-afraid-to-talk-about-xinygsqr9) · dltHub · 1 upvotes · 0 comments --- Tags: [#python](https://daily.dev/tags/python), [#rag](https://daily.dev/tags/rag), [#ollama](https://daily.dev/tags/ollama) [View this post on daily.dev](https://daily.dev/posts/automating-knowledge-graph-population-extracting-entities-and-triples-from-unstructured-text-with-a-occ2gvqzy) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Automating Knowledge Graph Population: Extracting Entities and Triples from Unstructured Text with an LLM","url":"https://daily.dev/posts/automating-knowledge-graph-population-extracting-entities-and-triples-from-unstructured-text-with-a-occ2gvqzy","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/automating-knowledge-graph-population-extracting-entities-and-triples-from-unstructured-text-with-a-occ2gvqzy"},"datePublished":"2026-09-29T12:50:25.545Z","dateModified":"2026-09-29T12:50:48.200Z","description":"A hands-on walkthrough shows how to build an automated pipeline that extracts structured entities and SPOC (Subject-Predicate-Object-Context) quads from...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/a142195d4af44b813762dffaa004cea6?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/a142195d4af44b813762dffaa004cea6?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Machine Learning Mastery","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Machine Learning Mastery","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/795781ffb32446c39eba040feb64f505","url":"https://daily.dev/sources/mlm"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/automating-knowledge-graph-population-extracting-entities-and-triples-from-unstructured-text-with-a-occ2gvqzy","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"python,rag,ollama","timeRequired":"PT4M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Machine Learning Mastery","item":"https://daily.dev/sources/mlm"},{"@type":"ListItem","position":3,"name":"Automating Knowledge Graph Population: Extracting Entities and Triples from Unstructured Text with an LLM"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/automating-knowledge-graph-population-extracting-entities-and-triples-from-unstructured-text-with-a-occ2gvqzy#faq","mainEntity":[{"@type":"Question","name":"How can I extract structured triples or quads from unstructured text using a local LLM instead of a paid API?","acceptedAnswer":{"@type":"Answer","text":"Run Llama 3.2 locally through Ollama and prompt it with few-shot examples in JSON output mode to extract Subject-Predicate-Object-Context (SPOC) quads from raw text such as Wikipedia articles. 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