What Does Named Entity Recognition Return for the London Tour Sentence?
You are developing a text processing solution.
You have the following function.
You call the function and use the following string as the second argument.
Our tour of London included a visit to Buckingham Palace
What will be the output of the function?
Community Insight
The exam tests distinguishing named-entity output from key-phrase or full-text output; the trap is counting ordinary nouns such as tour and visit as entities.
This AI-103 item tests named entity recognition in a text-processing function: the London tour sentence yields London and Buckingham Palace as location entities, so the output is those two values.
Many learners pick B because tour and visit look like key phrases, but key phrase extraction would not produce only those two terms and NER targets proper names like London and Buckingham Palace.
Community Discussion (1 comments)
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Expert Analysis
Why the Answer Is Correct
The function is performing named entity recognition over the supplied sentence, not returning the original text or a key-phrase list. In "Our tour of London included a visit to Buckingham Palace", the recognizable named entities are the location "London" and the location/landmark "Buckingham Palace". Therefore the output that lists only those two values matches the behavior of an entity-extraction function. Amelia233 captured the same reasoning: "The function is extracting named entities (proper nouns) from the text." Option A is the only choice consistent with that entity set.Why the Other Options Are Wrong
Option B lists "Tour and visit only"; those are common nouns, not named entities, and if key phrase extraction were used it would typically surface more than just those two words. Option C is the unchanged input string, which would only make sense if the function echoed or summarized the whole sentence rather than extracting entities. Option D mixes one valid location ("London") with an ordinary noun ("Tour"), so it cannot be the entity result. None of these matches the named-entity set of London and Buckingham Palace.Community Comment Notes
The visible learner comment from Amelia233 points directly to named-entity extraction and identifies London and Buckingham Palace as the proper nouns in the sentence. That aligns with official Azure AI Language NER behavior: locations are returned as entity spans, while ordinary words stay outside the entity list. No dissenting comment argues for key phrases or full-text output, so the community signal supports A.Official Reference
Exam Strategy
When a text-analysis item asks for output on a short sentence, first identify whether the function does NER, key phrase extraction, sentiment, or summarization, then filter the options through that operation. Proper nouns and place names are strong NER signals.
Frequently Asked Questions
Why isn't 'tour' returned as an entity?
NER returns named entities such as locations, people, and organizations; common nouns like tour and visit are not extracted by this operation.
Could the output be the full sentence instead?
Only if the function performed extractive summarization or echoed the input text; entity recognition returns entity spans, not the whole string.
Related Analysis
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