I think what complicates the analysis of the effect size(s) here is the repeated measures (or within-subjects) design. Because of this, the calculation of the effect sizes might be different from simple one-way ANOVA. This article compares different effect sizes for within- and between-subject designs. At the end of the article you will see that, it suggests, for within-subjects designs, "effect sizes that control for intra-subjects variability ($\eta^2_p$ and $\omega^2_p$), or that take the correlation between measurements into account (Cohen's dz)" (Lakens, 2013).
So, it might be better to take into consideration repeated measures design in your estimation of the effect size. But if we assume that your calculations are correct, Cohen's d (1.25) indicates a large effect size (Gamst et.al., 2008, p.44). However, the interpretation of the eta-squared depends on the context of your research.
I am not an SPSS user but as far as I understand SPSS already reports partial eta-squared ($\eta^2_p$). Here is an example of how to do ANOVA with repeated measures using SPSS (it also shows how to get effect size).
Gamst, G., Meyers, L. S., & Guarino, A. J. (2008). Analysis of Variance Designs: A Conceptual and Computational Approach with SPSS and SAS. Cambridge: Cambridge University Press.
Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4. http://doi.org/10.3389/fpsyg.2013.00863