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A pronoun such as he or his can refer to a person mentioned earlier. Our word cards preserve the paragraph’s order with SerialNumber. The task asks about meaning, so the nearest ordinary noun is not enough: “eyes”, “Monday” and “calendar” are nouns but not the person named Swaminathan.

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For this small demonstration, we explicitly recognize the person names Swaminathan and Vedanayagam. PartOfSpeech = Noun alone does not tell us which nouns are people. A larger language system would need more information. The nearest-person-name rule produces a candidate; later lessons here show when that rule fails.
The outer loop reads the paragraph forwards. When a relevant pronoun is found, the inner loop searches earlier words backwards. After finding a candidate, restore the searched cards, return to the marked pronoun and resume the outer loop after it. Without a position marker, you may repeat or skip words.

If the text has p pronouns and each backward scan may inspect up to n words, the search can revisit many cards. Physical restoration adds movements too. The order of a sentence is part of the information: changing it can change the meaning, unlike reordering cards when only their mark sum matters.
At word 11, “He considered”, why not choose “eyes” at word 10?
It is a noun, but not a person name. The simple person-name search passes it and reaches Swaminathan at position 4.
What should the procedure return if it reaches the start without a suitable name?
An explicit unresolved result. It should not invent a person or silently reuse an unrelated value.
Why keep SerialNumber when showing words in a table?
The position preserves text order and lets us locate the pronoun and preceding words even if another view is filtered or rearranged.