People v. Collazo
Opinion of the Court
—Judgment, Supreme Court, New York County (Patricia Williams, J., at hearing and jury trial; David Saxe, J., at sentence), rendered August 1, 1997, convicting defendant of burglary in the third degree and auto stripping in the third degree, and sentencing him, as a second felony offender, to concurrent terms of 3 to 6 years and one year, respectively, unanimously affirmed.
The court properly declined defendant’s request to charge the jury that the People were required to prove beyond a reasonable doubt that defendant knew that the van in question was used for commercial purposes. The culpable mental state of “knowingly,” for both trespass and burglary, applies to the element of entering or remaining unlawfully in or upon premises (Donnino, Practice Commentary, McKinney’s Cons Laws of NY, Book 39, Penal Law art 140, at 12). Burglary is an aggravated form of criminal trespass, adding the element of intent to commit a crime in a building or dwelling (id. at 5-6). Such aggravating circumstances “are factors to which a culpable mental state does not ordinarily attach” (People v Mitchell, 77 NY2d 624, 627). Moreover, the syntax of the third-degree burglary statute supports the foregoing, since the term “knowingly” is placed close to “unlawfully” and separates “knowingly” from the prepositional phrase, “in a building.” (Penal Law § 140.20.)
Since the disclosure, during jury deliberations, that a prosecution witness’s testimony that the company name appeared on the van at the time of the crime had been mistaken did not “create [ ] a reasonable doubt that did not otherwise exist” (United States v Agurs, 427 US 97, 112), defendant was not entitled to have the court instruct the jurors that a critical portion of the witness’s testimony was untrue, much less to a mistrial. A stipulation as to the mistake would have sufficed, but defendant requested no such remedy. Concur—Nardelli, J.P., Sullivan, Wallach and Rubin, JJ.
Case-law data current through December 31, 2025. Source: CourtListener bulk data.