Explain this named entity recognition code

Code:

#setting entity annotation

import spacy

from spacy.tokens import Span

nlp = spacy.load("en_core_web_sm")

doc = nlp("Video streaming subscriptions fall by two million")

ents = [(e.text, e.start_char, e.end_char, e.label_) for e in doc.ents]

print('Before', ents)

# The model didn't recognize "fb" as an entity :(

# Create a span for the new entity

fb_ent = Span(doc, 0, 1, label="ORG")

orig_ents = list(doc.ents)

# Option 1: Modify the provided entity spans, leaving the rest unmodified

doc.set_ents([fb_ent], default="unmodified")

# Option 2: Assign a complete list of ents to doc.ents

doc.ents = orig_ents + [fb_ent]

ents = [(e.text, e.start, e.end, e.label_) for e in doc.ents]

print('After', ents)

Output:

Before [('two million', 38, 49, 'CARDINAL')]

After [('Video', 0, 1, 'ORG'), ('two million', 5, 7, 'CARDINAL')]