While I enjoyed the long answer by alvas (well, they're suggesting you can only hit 100% accuracy with a rulebase; a man or woman after my own heart) I think the real problem the user asking the question, Merv Merzoug has, is not as complicated as it's made to be. Specifically, the user wants to parse successfully only a particular kind of English sentence - not arbitrary natural English sentences. What's more, those are sentences that are found in a particular, restricted context, that of strings that record travel details (most likely for some kind of travel agency and the like).

Given that those strings are going to be on the short side (you don't need to anticipate someone writing an epic saga in dactylic verse to describe a flight) the whole set of such sentences is probably finite and relatively small and can be represented by an equally finite and much smaller set of rules. So while alvas' comment is spot on regarding the amount of work one needs to do to cover all the exceptions and edge cases that might crop up even in such a small subset of natural English, it is not a problem so big as to be impossible to solve with some elbow grease.

There is much precedent of solving much more complex problems with hand-crafted grammar rules. For a very recent example, the latest online version of Magic the Gathering, M:tG Arena, apparently uses a hand-crafted context-free grammar to represent the language used on Magic cards [1]. The grammar is used in a parser that executes cards as scripts in a scripting language. Creating a grammar for the entire M:tG language is a job that a dedicated programming team can accomplish in a reasonable amount of time but even representing a small but substantial subset of that language is a goal that is in the range of, er, let's say ah a degree student of Computer Science in her final year :)

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[1] Just let me find that link. I got it around here somewhere...

Yep, here we go:

Magic Arena's GRE: The Groundwork for future digital products, in Chris Clay's Interview

To dispel any misconceptions: the GRP is not a machine learning approach to Natural Language Processing. It's more of a compiler/parser that uses rules and logic we've crafted. We have a dictionary that defines the parts of speech and some semantic data for "Magic Words". We also have a huge list of syntax rules for "Magic Sentences", similar to a Context Free Grammar. With this, and our good friend the Natural Language Toolkit, we generate a parse tree for ability text. The tree groups individual words into phrases, multiple phrases into sentences, and all the sentences under the ability as a whole. We then process that representation of an ability into higher-level ideas, specific to Magic. “Draw a card” is no longer just a verb and an object, but is an action and entity with regards to game play. We take verb 'atoms' (destroy, discard, exile) and use them to construct molecules ("triggered ability", "sequence of effects", "replacement effect"). With that semantic representation of the ability, we can then basically compile it into code. That code is then added to the GRE,and executes when you play Arena!

https://www.reddit.com/r/magicTCG/comments/74hw1z/magic_aren...