{"id":"W4407873161","doi":"10.2139/ssrn.5152241","title":"Context-Aware Entity-Relation Extraction Pipeline for Threat Intelligence Knowledge Graphs","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Pipeline (software); Context (archaeology); Computer science; Relation (database); Knowledge graph; Relationship extraction; Extraction (chemistry); Entity linking; Artificial intelligence; Data science; Knowledge management; Data mining; Knowledge base; Programming language; Geography; Chemistry; Chromatography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001014765,0.001452897,0.001188568,0.007154641,0.001263139,0.003273213,0.001663909,0.001539832,0.01066479],"category_scores_gemma":[0.00697675,0.0007528792,0.001918458,0.005569255,0.0003866579,0.003817525,0.002668869,0.002076558,0.007625467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007490708,"about_ca_system_score_gemma":0.001997658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007631676,"about_ca_topic_score_gemma":0.01598537,"domain_scores_codex":[0.9991218,0.0001036072,0.00008988292,0.0003129707,0.0002920957,0.00007958023],"domain_scores_gemma":[0.9976439,0.001041991,0.0001871319,0.000604555,0.0004112818,0.0001112426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004018477,0.0004682273,0.008266366,0.001944773,0.0003850635,0.00122157,0.001006909,0.01523068,0.02947177,0.02603104,0.113589,0.8019828],"study_design_scores_gemma":[0.0001041238,0.0002602,0.01558455,0.000716093,0.0006736521,0.001955653,0.001294444,0.5000578,0.0698338,0.1626734,0.2466466,0.000199785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02075083,0.002213462,0.8418635,0.001690545,0.0002197626,0.0008079425,0.03493766,0.08912013,0.008396295],"genre_scores_gemma":[0.1504607,0.001748856,0.7741491,0.000584588,0.0001262446,0.0003131345,0.06448417,0.002145955,0.005987225],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01066479,"threshold_uncertainty_score":0.03567725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.12341697991583,"score_gpt":0.4437445261267874,"score_spread":0.3203275462109574,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}