Light verb constructions (LVCs) are a challenging class of verbal multiword expressions, especially in Turkish,where rich morphology and productive complex predicates create minimal contrasts between idiomatic predicatemeanings and literal verb–argument uses. This paper asks what signals drive LVC classification bysystematically restricting model inputs. Using UD-derived supervision, we compare lemma-driven baselines(lemma TF–IDF + Logistic Regression; BERTurk trained on lemma sequences), a grammar-only Logistic Regressionover UD morphosyntax (UPOS/DEPREL/MORPH), and a full-input BERTurk baseline. We evaluate on a controlleddiagnostic set with Random negatives, lexical controls (NLVC), and LVC positives, reporting split-wiseperformance to expose decision-boundary behavior. Results show that coarse morphosyntax alone is insufficientfor robust LVC detection under controlled contrasts, while lexical identity supports LVC judgments but issensitive to calibration and normalization choices. Overall, our findings motivate targeted evaluation forTurkish MWEs and highlight that “lemma-only” is not a single representation but depends critically on hownormalization is instantiated.

Turkish idiomatic light verb constructions (LVCs) are challenging for multiword expression processing because they often share the same surface form as fully literal verb–object combinations while functioning as a single, partially idiomatic predicate. We frame Turkish LVC detection as a binary classification task (literal meaning vs. idiomatic meaning) and evaluate on a manually created controlled set (N=147) with matched negatives: out-of-domain random sentences and in-domain literal controls (NLVC), alongside LVC positives. We compare a supervised Turkish encoder baseline (BERTurk with a classifier head) to three instruction-tuned LLMs from different families under zero-shot, one-shot, and few-shot prompting, and analyze how demonstrations shift error profiles. In zero-shot, LLMs perform well on negatives but show very low LVC recall. One-shot prompting sharply improves LVC detection but can induce strong, model-specific biases (over- vs. under-predicting LVC). A richer few-shot prompt improves calibration and yields robust overall performance for GPT-OSS-20B and Qwen 2.5-14B. Overall, the results highlight substantial prompt sensitivity in Turkish metalinguistic classification: the supervised baseline remains competitive, while prompted LLMs can match or exceed it on LVCs with carefully constructed demonstrations. We release code, prompts, and evaluation materials to support reproducibility.