We describe the SalahAbdo closed-track systems for both Daleel 2026 subtasks: deciding which argumentative-unit types appear in an Arabic paragraph (Task 1) and locating their exact spans (Task 2), in editorials and debates. No single method was best for all six argumentative-unit types, so our Task 1 system routes each type to the method that measured best for it: assumption (AS), testimony (TE), and statistics (ST) are detected by sampling a gemma-4-31b classifier five times and firing when enough samples agree; anecdote (AN) and other (OT) by a fine-tuned CAMeLBERT classifier; and the lowest-agreement type, common ground (CO), by ranking paragraphs and firing a fixed count. A final verifier shows the LLM examples of true and false ST paragraphs and removes likely false positives. Task 2 first extracts spans as verbatim LLM quotes, then re-labels them by blending encoder probabilities with label-sequence statistics under Viterbi decoding, with zero additional LLM calls. Every design change had to clear an adoption bar set before its result was read: 29 recorded decisions produced six adoptions, and composition arithmetic over already-scored legs predicted two development scores digit-for-digit. Development scores are 0.7089 macro-F1 (Task 1) and 0.7247 span F1 (Task 2); the pinned recipes scored 0.6499 and 0.7055 on test, and our final official submissions scored 0.712 and 0.7316: first of ten and fifth of eight closed-track teams (Section 6 discloses the evaluation-phase protocol). Residual errors track the scheme’s measured human agreement (kappa as low as 0.114): types whose definitions a model can check against the text improved under LLM verification, while types defined by annotation convention resisted every prompting attack and were learned only from supervision and sequence structure.

We describe Team Wasl’s systems for the KnowledgeGraphEval 2026 shared task at ArabicNLP 2026, which decomposes Arabic knowledge-graph construction into two evaluated stages: nested NER under domain shift and relation extraction between given mentions. For Subtask 1 (AdaptNER), we train a multi-head BIO tagger (one three-way O/B/I head per entity type over a shared pretrained Arabic encoder) on Wojood and combine three random seeds by span-level majority vote. For Subtask 2, forced-choice classification over the 40 WojoodRelations relation types, we fine-tune a typed-marker classifier with type-constraint decoding, ensembling five grouped folds by logit averaging. Our submissions scored 0.7483 micro-F1 on AdaptNER’s blind Konooz slice (ten unseen domains), 4th of the eight officially ranked teams and 11.8 points above the official baseline, and 0.9571 micro-F1 on relation extraction with the no-relation class masked at decoding, 2nd at the results release; re-scored by the organizers over all 41 classes, the same ensemble reaches 0.9328, 4th in the organizers’ final ranking. We analyze where the 18.6-point domain-shift tax on NER falls: chiefly on recall. We further show that type-constraint decoding and ensembling are partially redundant (+0.9 points on a single RE model, +0.1 on the ensemble) and that the surviving errors share an argument-type signature that no type constraint can separate.