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ARXIV:2604.11104 · KNOWLEDGE GRAPH CONSTRUCTION · SUBMITTED 15 APR · 16:47 UTC · FRESHNESS STALE
ARXIV:2604.11104KNOWLEDGE GRAPH CONSTRUCTIONSUBMITTED 15 APR · 16:47 UTCFRESHNESS STALEPierre Jourlin · arXiv
A zero-shot pipeline for frugal knowledge graph construction using local LLMs, achieving competitive results with consumer hardware.
Opportunity summary
Pain A zero-shot pipeline for frugal knowledge graph construction using local LLMs, achieving competitive results with consumer hardware.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence unverified
A zero-shot pipeline for frugal knowledge graph construction using local LLMs, achieving competitive results with consumer hardware. We propose a reproducible evaluation framework integrating two external benchmarks (DocRED, HotpotQA), WebQuestionsSP-style synthetic data, and the…
This paper presents an empirical study of a multi-model zero-shot pipeline for knowledge graph construction and exploitation, executed entirely through local inference on consumer-grade hardware. We propose a reproducible evaluation framework integrating two external…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. On 500 document-level relations, our system achieves an F1 of 0.70 $\pm$ 0.041 in zero-shot, compared to 0.80 for supervised DREEAM. A public repository…
Knowledge Graph Construction moved forward this cycle; last verified April 2026. Public score 6.0/10. Implementation evidence is present through a linked repository.
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Score6.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A zero-shot pipeline for frugal knowledge graph construction using local LLMs, achieving competitive results with consumer hardware.
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10.48550/arXiv.2604.11104A zero-shot pipeline for frugal knowledge graph construction using local LLMs, achieving competitive results with consumer hardware.
Abstract
This paper presents an empirical study of a multi-model zero-shot pipeline for knowledge graph construction and exploitation, executed entirely through local inference on consumer-grade hardware. We propose a reproducible evaluation framework integrating two external benchmarks (DocRED, HotpotQA), WebQuestionsSP-style synthetic data, and the RAGAS evaluation framework in an automated pipeline. On 500 document-level relations, our system achieves an F1 of 0.70 $\pm$ 0.041 in zero-shot, compared to 0.80 for supervised DREEAM. Text-to-query achieves an accuracy of 0.80 $\pm$ 0.06 on 200 samples. Multi-hop reasoning achieves an Exact Match (EM) of 0.46$\pm$0.04 on 500 HotpotQA questions, with a RAGAS faithfulness of 0.96 $\pm$ 0.04 on 50 samples. Beyond the pipeline, we study diversity mechanisms for difficult multi-hop reasoning. On 181 questions unsolvable at zero temperature, self-consistency (k=5, T =0.7) recovers up to 23% EM with a single Mixture-of-Experts (MoE) model, but the cross-model oracle (3 architectures x 5 samples) reaches 46.4%. We highlight an agreement paradox: strong consensus among samples signals collective hallucination rather than a reliable answer, echoing the work of Moussa{ï}d et al. on the wisdom of crowds. Extending to the full pipeline (500 questions), self-consistency (k=3) raises EM from 0.46 to 0.48 $\pm$ 0.04. A confidence-routing cascade mechanism (Phi-4 $\rightarrow$ GPT-OSS, k=5) achieves an EM of 0.55 $\pm$ 0.04, the best result obtained, with 45.4% of questions rerouted. Finally, we show that V3 prompt engineering applied to other models does not reproduce the gains observed with Gemma-4, confirming the specific prompt/model interaction. The entire system runs in $\sim$5 h on a single RTX 3090, without any training, for an estimated carbon footprint of 0.09 kg CO2 eq.
Source availability
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Extraction status
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Proof status
unverified0 refs; 4 sources; 67% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 6.0
PROBLEM
A zero-shot pipeline for frugal knowledge graph construction using local LLMs, achieving competitive results with consumer hardware. We propose a reproducible evaluation framework integrating two external benchmarks (DocRED, HotpotQA), WebQuestionsSP-style synthetic data, and th...
METHOD
This paper presents an empirical study of a multi-model zero-shot pipeline for knowledge graph construction and exploitation, executed entirely through local inference on consumer-grade hardware. We propose a reproducible evaluation framework integrating two external benchmarks...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. On 500 document-level relations, our system achieves an F1 of 0.70 $\pm$ 0.041 in zero-shot, compared to 0.80 for supervised DREEAM. A public repository is linked, so build verification can inspect implem...
WHY NOW
Knowledge Graph Construction moved forward this cycle; last verified April 2026. Public score 6.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A zero-shot pipeline for frugal knowledge graph construction using local LLMs, achieving competitive results with consumer hardware. We propose a reproducible evaluation framework integrating two external benchmarks (DocRED, HotpotQA), WebQuestionsSP-style synthetic data, and the RAGAS evaluation framework in an automated pipeline.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
This paper presents an empirical study of a multi-model zero-shot pipeline for knowledge graph construction and exploitation, executed entirely through local inference on consumer-grade hardware. We propose a reproducible evaluation framework integrating two external benchmarks (DocRED, HotpotQA), WebQuestionsSP-style synthetic data, and the RAGAS evaluation framework in an automated pipeline.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. On 500 document-level relations, our system achieves an F1 of 0.70 $\pm$ 0.041 in zero-shot, compared to 0.80 for supervised DREEAM. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Knowledge Graph Construction moved forward this cycle; last verified April 2026. Public score 6.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
Materials
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Competitors
A zero-shot pipeline for frugal knowledge graph construction using local LLMs, achieving competitive results with consumer hardware.
Segment
Knowledge Graph Construction
Adoption evidence
Public code linked for build inspection
Commercial read
6.0/10 public viability
Direct
Adjacent
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CITED BY
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
Build passport not yet generated
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 4 sources / 67% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 4 sources, 67% evidence coverage.
Gaps
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Buyer clarity
missing
Current read
No budget owner is verified for this paper.
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Gaps
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Defensibility
missing
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Defensibility signals are missing.
Evidence
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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Regulatory load
missing
Current read
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Evidence
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Gaps
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Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
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