In several (but not all) cases, it could detect repeated actions and added the corresponding actions several times. For example, in the procedure that read "After washing the precipitate with cold water (2×25 mL) […]" (Example 6 in Table ), it was the only model that created the annotation "<WASH> cold water 25 mL cold water 25 mL", with the other models annotating this action as "<WASH> cold water 2x25 mL". Likewise, in the procedure that read "[…] washed 2x with water (2x 90 mL), 2x with ethanol (2x 90 mL) and 2x with toluene (2x 90 mL) […]" (Example 1 in Table ), it created the annotation "<WASH> water 2x 90 mL water 2x 90 mL ethanol 2x 90 mL ethanol 2x 90 mL toluene 2x 90 mL", i.e., correctly repeating the washing steps with water and ethanol twice. Strangely it did not do the same for toluene though. While the correct meaning of repeating the action could perhaps also be deduced in a post-processing step from the fact that the volumes in the annotations created by the other models have a "2x" modifier, it is interesting that the BigBirdPegasus model was the only one to create two separate actions here. Even more remarkable is the annotation it produced for the part of the procedure that read "[X] and [Y] were suspended in a 1:1 mixture of water and tert-butyl alcohol (12 mL)" (Example 6 in Table ). Here, it produced the annotation "<ADD> [X] [Y] water 6 mL tert-butyl alcohol 6 mL", i.e., correctly reasoning that 12 mL of a 1:1 mixture of water and tert-butyl alcohol implies adding 6 mL of water and 6 mL of tert-butyl alcohol.