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Paper Citation Record · LEDGER

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction

As of 10 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2601.15037.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2601.15037 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T09:02:59.078398Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

54 of 54 outbound references displayed

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External citation measurements

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Outbound references

Observation a5a61224-6882-4cb0-aa13-98f2629eba51 · outbound

This paper cites Knowledge graph based synthetic corpus generation for knowledge-enhanced language model pre-training.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Knowledge graph based synthetic corpus generation for knowledge-enhanced language model pre-training

Reference 1

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source=arxiv_source observed=2026-08-03T09:02:55.053110Z digest=sha256:23f924c4bff031e565e4f4623a714e9b24e690779188c995c87686c3131d6b31

Observation fbe1daef-1cd6-479b-9e33-360b470eba0c · outbound

This paper cites Cafarella, Stephen Soderland, Matthew Broadhead, and Oren Etzioni.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Cafarella, Stephen Soderland, Matthew Broadhead, and Oren Etzioni

Reference 2

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source=arxiv_source observed=2026-08-03T09:02:55.091563Z digest=sha256:da64167c7abd4467d14b3b5ca3b108add463201c77edd2cad6511ecd18f3d6f7

Observation caacd6d6-0a7e-4b9f-93ea-bb525216cdbd · outbound

This paper cites Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, et al.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, et al

Reference 3

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source=arxiv_source observed=2026-08-03T09:02:55.153715Z digest=sha256:2cfb68b1048765f6c6ecd33e6ea3b27429458a2737f7ee6220837ec345f7d3aa

Observation 89905702-f4e4-45c8-bdeb-16251339ee5e · outbound

This paper cites REBEL: relation extraction by end-to-end language generation.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction REBEL: relation extraction by end-to-end language generation

Reference 4

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source=arxiv_source observed=2026-08-03T09:02:55.224962Z digest=sha256:b0ac44594bb60f7c1dbf2f5a01d90571e1f1d22d83ecb2ce938933de14854625

Observation edd27948-90ec-410a-9850-af61e1518a26 · outbound

This paper cites Exploiting syntactico-semantic structures for relation extraction.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Exploiting syntactico-semantic structures for relation extraction

Reference 5

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source=arxiv_source observed=2026-08-03T09:02:55.306519Z digest=sha256:16c38742dad2f0e51f66b322d2662dc80239f20f7b70cb14c67c5a1c9412962f

Observation 6defc8ad-c440-4167-8a52-2cb42b328dba · outbound

This paper cites SAC-KG: exploiting large language models as skilled automatic constructors for domain knowledge graph.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction SAC-KG: exploiting large language models as skilled automatic constructors for domain knowledge graph

Reference 6

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source=arxiv_source observed=2026-08-03T09:02:55.367481Z digest=sha256:185203cfa86e9f314d929a0409caa285d3da0a6cf850c07ba24e6e30c121a7dc

Observation 454a0215-9bb7-4edd-9e5a-b649402fe718 · outbound

This paper cites M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

Reference 7

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source=arxiv_source observed=2026-08-03T09:02:55.441644Z digest=sha256:2ba7ad22b5dd4878b041fad51e22bef928a346a5b78c01e58394fb4fe65b97ad

Observation 56b00b1a-9d3a-4188-96a3-bcf763ada314 · outbound

This paper cites Overview of MUC-7.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Overview of MUC-7

Reference 8

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Observation de63fd31-a068-4272-9908-cc3501e7c691 · outbound

This paper cites Unsupervised cross-lingual representation learning at scale.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Unsupervised cross-lingual representation learning at scale

Reference 9

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Observation e3c1ba27-002d-41bc-a32b-95be23b2116c · outbound

This paper cites Rlprompt: Optimizing discrete text prompts with reinforcement learning.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Rlprompt: Optimizing discrete text prompts with reinforcement learning

Reference 10

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Observation f6f3f7a0-93c2-444e-922a-e1450686d019 · outbound

This paper cites Span-level model for relation extraction.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Span-level model for relation extraction

Reference 11

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Observation e57be485-701b-4c27-bcfe-544e9fd78bf3 · outbound

This paper cites Dognin, Inkit Padhi, Igor Melnyk, and Payel Das.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Dognin, Inkit Padhi, Igor Melnyk, and Payel Das

Reference 12

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Observation 63eea9cf-5960-472a-aab5-baeef86e024f · outbound

This paper cites From Local to Global: A Graph RAG Approach to Query-Focused Summarization.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Reference 13

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Observation 1cffee2a-3ca9-4209-a673-15868327280b · outbound

This paper cites The 2020 bilingual, bi-directional webnlg+ shared task overview and evaluation results (webnlg+ 2020).

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction The 2020 bilingual, bi-directional webnlg+ shared task overview and evaluation results (webnlg+ 2020)

Reference 14

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Observation f2ae3f55-d7a6-4954-a34d-8d321dcc80f9 · outbound

This paper cites Suchanek.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Suchanek

Reference 15

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Observation 4ebbccdc-30c6-46cf-9864-ff7e96e5ea25 · outbound

This paper cites PPDB: the paraphrase database.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction PPDB: the paraphrase database

Reference 16

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Observation 847686d6-fced-41db-be89-b58cd57a32c6 · outbound

This paper cites Table filling multi-task recurrent neural network for joint entity and relation extraction.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Table filling multi-task recurrent neural network for joint entity and relation extraction

Reference 17

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Observation a282ed9d-c653-42d4-a88e-04229edb6c24 · outbound

This paper cites Knowledge Graphs.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Knowledge Graphs

Reference 18

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Observation b0fb52c9-8a31-4dc6-a5f3-383ba20c8c24 · outbound

This paper cites Relink: Constructing query-driven evidence graph on-the-fly for graphrag.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Relink: Constructing query-driven evidence graph on-the-fly for graphrag

Reference 19

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Observation a4884f5e-3907-49bd-9f30-e46a8307245b · outbound

This paper cites Xu, Jun Araki, and Graham Neubig.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Xu, Jun Araki, and Graham Neubig

Reference 20

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Observation f6e97899-fb45-4b59-98ba-a5fe568ec9ea · outbound

This paper cites Genie: Generative information extraction.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Genie: Generative information extraction

Reference 21

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Observation a8b667df-283b-4385-9275-325dfd4d2fe0 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction The power of scale for parameter-efficient prompt tuning

Reference 22

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Observation 51833f10-4d5b-4cea-8ccc-e8649c739bf1 · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Prefix-tuning: Optimizing continuous prompts for generation

Reference 23

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Observation 0d6455b1-2418-4435-8c07-89f58d010fdf · outbound

This paper cites P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks

Reference 24

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Observation 26d5b7ba-c4e6-4dbb-81cf-4e35f8704242 · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 25

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Observation ccc7734d-c9f1-4e24-8ffa-238de6f54e22 · outbound

This paper cites an unresolved cited work.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Unresolved cited work

Reference 26

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Observation e333490c-ccb1-4472-803a-e053f82711f2 · outbound

This paper cites Kanatsoulis, and Sanmi Koyejo.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Kanatsoulis, and Sanmi Koyejo

Reference 27

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Observation 04f90316-f4a7-4855-87a8-a86ff7f1dc3e · outbound

This paper cites Pan, Guido Vetere, Jos \' e Manu \' e l G \' o mez - P \' e rez, and Honghan Wu.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Pan, Guido Vetere, Jos \' e Manu \' e l G \' o mez - P \' e rez, and Honghan Wu

Reference 28

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Observation dd9773a5-f683-4a99-8d48-7cd56dd5a78c · outbound

This paper cites Unifying large language models and knowledge graphs: A roadmap.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Unifying large language models and knowledge graphs: A roadmap

Reference 29

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Observation 46d23486-0bf5-46d1-81f8-39f34908599e · outbound

This paper cites Aligning open IE relations and KB relations using a siamese network based on word embedding.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Aligning open IE relations and KB relations using a siamese network based on word embedding

Reference 30

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Observation 23b5b6af-f209-4416-8336-b6dbb3317e4e · outbound

This paper cites Onerel: Joint entity and relation extraction with one module in one step.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Onerel: Joint entity and relation extraction with one module in one step

Reference 31

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Observation e2de6665-e39f-4ef8-b90e-09310a148bf5 · outbound

This paper cites Logan IV, Eric Wallace, and Sameer Singh.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Logan IV, Eric Wallace, and Sameer Singh

Reference 32

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source=arxiv_source observed=2026-08-03T09:02:57.065145Z digest=sha256:1deff190688089415a368e5e9feb226013c4ca0061aa5163a07b003aaa30b245

Observation bd3b7bc4-09d8-4a8a-9ea2-12d5dc2a2066 · outbound

This paper cites Overview of results of the MUC-6 evaluation.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Overview of results of the MUC-6 evaluation

Reference 33

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Observation bdf461b7-abff-4471-b2b6-b8a95b9125c2 · outbound

This paper cites Neural relation extraction for knowledge base enrichment.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Neural relation extraction for knowledge base enrichment

Reference 34

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Observation a8f5115b-1cb8-4bb7-91cf-b0d05d8690f0 · outbound

This paper cites Talukdar.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Talukdar

Reference 35

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source=arxiv_source observed=2026-08-03T09:02:57.319147Z digest=sha256:b271b6d5241b75ff5cb1832ade627cadda43642c078608e08b20b8af3a262f17

Observation 97dddb0b-7e0f-46cd-8de2-ddfff0c59654 · outbound

This paper cites an unresolved cited work.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Unresolved cited work

Reference 36

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Observation f44fb66f-e1d7-4927-acd2-f7e3186b2a96 · outbound

This paper cites Tplinker: Single-stage joint extraction of entities and relations through token pair linking.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Tplinker: Single-stage joint extraction of entities and relations through token pair linking

Reference 37

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Observation 655eabc1-80e9-4cfc-a0f1-395037ae3fbe · outbound

This paper cites A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects

Reference 38

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Observation 3a833c7e-93ff-4bb3-9b56-b332dcd02be5 · outbound

This paper cites Ime: Integrating multi-curvature shared and specific embedding for temporal knowledge graph completion.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Ime: Integrating multi-curvature shared and specific embedding for temporal knowledge graph completion

Reference 39

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Observation f9fd49cc-5682-4351-8f0e-a39644bd8fb4 · outbound

This paper cites Large language models-guided dynamic adaptation for temporal knowledge graph reasoning.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Large language models-guided dynamic adaptation for temporal knowledge graph reasoning

Reference 40

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Observation 7bc022a2-60ca-49d4-9004-5d85f756af47 · outbound

This paper cites ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT

Reference 41

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Observation 5dbd8a64-0666-481b-90f4-39d05645a8f4 · outbound

This paper cites Explicit semantic ranking for academic search via knowledge graph embedding.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Explicit semantic ranking for academic search via knowledge graph embedding

Reference 42

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Observation 818238d8-9351-4480-950b-e72b39ec80c9 · outbound

This paper cites Large language models for generative information extraction: a survey.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Large language models for generative information extraction: a survey

Reference 43

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Observation 4bd794b1-3fbc-49d2-8be0-a2e6482c6b12 · outbound

This paper cites Large Language Models as Optimizers.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Large Language Models as Optimizers

Reference 44

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Observation 7fc63274-2235-4bad-9453-da8b2f0331a7 · outbound

This paper cites u ksekg \.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction u ksekg \

Reference 45

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source=arxiv_source observed=2026-08-03T09:02:58.168522Z digest=sha256:823e4aa80406eb10344f5480154bd83e11ed72c5f993c5112deac2a8f084c899

Observation 8afcd9f3-b7c8-45c0-a16b-b3ffb36d7d56 · outbound

This paper cites Kernel methods for relation extraction.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Kernel methods for relation extraction

Reference 46

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source=arxiv_source observed=2026-08-03T09:02:58.294741Z digest=sha256:d9a9e3d95841c5ea07bb2fce0cd4ebb76efb33fabeb3d3646a9ab011998d1fa4

Observation 363cac16-a09e-4d67-b4e1-33d3211c7b9f · outbound

This paper cites Extract, define, canonicalize: An llm-based framework for knowledge graph construction.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Extract, define, canonicalize: An llm-based framework for knowledge graph construction

Reference 47

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source=arxiv_source observed=2026-08-03T09:02:58.364436Z digest=sha256:42e8a02bd229a4de4b7e9092f341b1e317be3f3aa13c9596e281bc4830b01d90

Observation 7157138e-1172-48d7-87c3-29bd606c85b0 · outbound

This paper cites Smola, and Le Song.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Smola, and Le Song

Reference 48

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Observation 937e7247-2f52-4d06-af92-bd4210e2e718 · outbound

This paper cites Large language models are human-level prompt engineers.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Large language models are human-level prompt engineers

Reference 49

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source=arxiv_source observed=2026-08-03T09:02:58.580950Z digest=sha256:49f71c21224e977a9ea73e5b8c519e1a34e70bdbb7ff93b6abcb2344465f7688

Observation 1313dc85-4474-4fcc-ac10-7e8d29ce6c42 · outbound

This paper cites Llms for knowledge graph construction and reasoning: recent capabilities and future opportunities.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Llms for knowledge graph construction and reasoning: recent capabilities and future opportunities

Reference 50

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source=arxiv_source observed=2026-08-03T09:02:58.667022Z digest=sha256:b9b67a7d89ccdfd49898900a005ae5f6964d7da9e33a32af9e80d8ace8b3ff5d

Observation 5178031e-d1a0-476f-9273-da4d9f3f7139 · outbound

This paper cites Geometric-contextual mutual infomax path aggregation for relation reasoning on knowledge graph.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Geometric-contextual mutual infomax path aggregation for relation reasoning on knowledge graph

Reference 51

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source=arxiv_source observed=2026-08-03T09:02:58.769177Z digest=sha256:eacfcbf40c48f585e9f3b0710adeb612388e5030d81197d5159adfd9aecf59cd

Observation 584d5d01-d037-4387-9d27-394eff912f31 · outbound

This paper cites Progressive prefix-memory tuning for complex logical query answering on knowledge graphs.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Progressive prefix-memory tuning for complex logical query answering on knowledge graphs

Reference 52

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source=arxiv_source observed=2026-08-03T09:02:58.869021Z digest=sha256:b73cbd8120ecceb020e75452893b0069ba21e746048326de6046ad17df022029

Observation 04105a25-130e-4039-a291-3879220b6c75 · outbound

This paper cites Effective instruction parsing plugin for complex logical query answering on knowledge graphs.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction Effective instruction parsing plugin for complex logical query answering on knowledge graphs

Reference 53

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source=arxiv_source observed=2026-08-03T09:02:58.967470Z digest=sha256:7ec28a498cefd22a2be26f99d3b17582ca8b639f0863f04a7d8af25bf24d5a52

Observation 2b88e711-7e66-453f-8a04-8db195f1d145 · outbound

This paper cites write newline.

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction write newline

Reference 54

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source=arxiv_source observed=2026-08-03T09:02:59.078398Z digest=sha256:c98fe86680e672372d571bb7c0bf3402d0a20c6a8b7e65b2c201dfbb1753b765

Pith citing papers

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