\section{Analysis}

Overall, our results point to two conclusions. First, \textit{hard-to-learn} DiscoGeM instances cannot be reliably interpreted as erroneous, as agreement level and label frequency are two factors that significantly impact the \textbf{confidence} parameter while not directly translating into annotation quality. Rather, they point to item ambiguity, and filtering out \textit{hard-to-learn} items would thus mean removing the ambiguous part, which dataset cartography normally suggests to keep for training.

On the other hand, the \textit{easy-to-learn} region makes a better candidate for filtering, since a substantial proportion of temporal labels can be recovered from cues. This effect is also exacerbated on more granular levels. This model behavior is reminiscent of heuristic learning reported for NLI, where models regularly learn lexical or syntactic shortcuts \cite{feng_misleading_2019,mccoy_right_2019}. 
% While cue-driven temporal instances in DiscoGeM are not strictly erroneous, they do not require a high level of pragmatic inference compared to the other relation types.

% Reliance on cues could also explain why finer temporal labels are not necessarily harder for the models. The common expectation is that coarseness translates directly into easier inference, but our results show that granularity can sharpen the boundaries for lexically marked classes.
% and enable learning with high confidence on levels 2 and 3. Particularly, discriminating between relations like \textit{precedence} and \textit{succession} results in higher confidence. 
% This may indicate that recent efforts to classify relations at the most granular level \cite{costa-kosseim-2024-exploring} or at all levels jointly \cite{costa-kosseim-2025-multi}, are justified even if this diverges from common practice. 

% The methodological implication is that data maps for IDRR are useful as a diagnostic of dataset structure, but not as a straightforward cleaning heuristic. Its main value lies in separating disagreement-heavy items from cue-rich subsets that inflate apparent ease. 
These findings highlight the potential of dataset cartography in IDRR and other NLU tasks where the class structure is highly imbalanced. Although data maps cannot be directly applied to filter out uncertain items, they can serve as an interpretable tool for identifying shortcuts. 
