---
title: Identify the substance contained in both cigarettes and electronic c… — Smoking, vaping and betting: habits th… · tree and pragmatics
description: Identify the substance contained in both cigarettes and electronic cigarettes which renders them addictive. Exam question 1 of lesson 28 «Smoking, vaping and betting: habits that are hard to stop» of the adult life course of Mova's primary school, with the dependency tree and pragmatics of the quest
---

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# Exam question 1 · Smoking, vaping and betting: habits that are hard to stop

<!-- .ven lang=en -->
Identify the substance contained in both cigarettes and electronic cigarettes which renders them addictive.

<!-- .vintro -->
**Mova's choice:** option 1 · **The right answer:** option 2 · wrong ✗ · **Who acts:** you, substance · **What happens:** identify, contain, render · **To what, where:** substance, cigarettes

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This is exam question 1 of lesson 28 «Smoking, vaping and betting: habits that are hard to stop» of the adult life course of Mova's primary school, asked in stiff, formal language. The page gives the question's syntax tree — a dependency tree in the style of Universal Dependencies — and its pragmatics clause by clause, then the 4 options as the student saw them, each with its tree and pragmatics. Every text is parsed by Mova itself. How the student answered it, with the score of every option and why: [the test at school](https://md.mova.elamur.ai/free/en/school-run/adult-life/28-smoking-vaping-and-betting/#e1).

## The question {#question}

```conllu
# sent_id = question:adult-life:28-smoking-vaping-and-betting:e1:1
# text = Identify the substance contained in both cigarettes and electronic cigarettes which renders them addictive.
1	Identify	identify	VERB	VB	Mood=Imp|VerbForm=Fin	0	root	_	_
2	the	the	DET	DT	Definite=Def|PronType=Art	3	det	_	_
3	substance	substance	NOUN	NN	Number=Sing	1	obj	_	_
4	contained	contain	VERB	VBN	Tense=Past|VerbForm=Part|Voice=Pass	3	acl	_	_
5	in	in	ADP	IN	_	7	case	_	_
6	both	both	DET	DT	PronType=Tot	7	det	_	_
7	cigarettes	cigarette	NOUN	NNS	Number=Plur	4	obl	_	_
8	and	and	CCONJ	CC	_	10	cc	_	_
9	electronic	electronic	ADJ	JJ	Degree=Pos	10	amod	_	_
10	cigarettes	cigarette	NOUN	NNS	Number=Plur	7	conj	_	_
11	which	which	PRON	WDT	PronType=Rel	12	nsubj	_	_
12	renders	render	VERB	VBZ	Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin	3	acl:relcl	_	_
13	them	they	PRON	PRP	Case=Acc|Number=Plur|Person=3|PronType=Prs	12	obj	_	_
14	addictive	addictive	ADJ	JJ	Degree=Pos	12	xcomp	_	SpaceAfter=No
15	.	.	PUNCT	.	_	1	punct	_	_
```

<!-- .fr -->
| # | verb | tense, mood | who | what, whom | to whom | where | when | other | roles by |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | **identify** | imperative | you *not said here* | the substance |  |  |  |  | vn characterize-29.2-1-1 «NP V NP S\_ING»: agent=Agent substance=Theme |
| 2 | **contain** | participle |  | the substance *not said here* |  | both cigarettes and electronic cigarettes |  |  | vn fit-54.3 «NP V NP PP.location»: agent=Agent in cigarette=Location |
| 3 | **render** | present | the substance | cigarettes *= cigarette* |  |  |  | addictive *property* | vn give-13.1 «NP V NP»: agent=Agent they=Theme |

<!-- .note -->
One row per clause the reader finds. «not said here»: a role filled from the clause above. «roles by»: a VerbNet class and frame when one fits the verb, else the reader's rules on the tree.

## The options {#options}

### Option 1 · ← Mova's choice {#o1}

<!-- .ven lang=en -->
Water vapour

```conllu
# sent_id = question:adult-life:28-smoking-vaping-and-betting:e1:o1:1
# text = Water vapour
1	Water	water	NOUN	NN	Number=Sing	2	compound	_	_
2	vapour	vapour	NOUN	NN	Number=Sing	0	root	_	_
```

<!-- .note -->
The frames reader finds no event or fact in this sentence.

### Option 2 · ✓ the right answer {#o2}

<!-- .ven lang=en -->
Nicotine

```conllu
# sent_id = question:adult-life:28-smoking-vaping-and-betting:e1:o2:1
# text = Nicotine
1	Nicotine	nicotine	NOUN	NN	Number=Sing	0	root	_	_
```

<!-- .note -->
The frames reader finds no event or fact in this sentence.

### Option 3 {#o3}

<!-- .ven lang=en -->
Sugar

```conllu
# sent_id = question:adult-life:28-smoking-vaping-and-betting:e1:o3:1
# text = Sugar
1	Sugar	sugar	NOUN	NN	Number=Sing	0	root	_	_
```

<!-- .note -->
The frames reader finds no event or fact in this sentence.

### Option 4 {#o4}

<!-- .ven lang=en -->
Caffeine

```conllu
# sent_id = question:adult-life:28-smoking-vaping-and-betting:e1:o4:1
# text = Caffeine
1	Caffeine	caffeine	NOUN	NN	Number=Sing	0	root	_	_
```

<!-- .note -->
The frames reader finds no event or fact in this sentence.

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**Text:** the tests of Mova's primary school, written for Mova (`world/data/school/`), as the course runner gave them to the student. **Trees:** Mova's own parse — the `en` model on the CPU, when the site is built — not checked by a teacher; they may be wrong. **Frames:** Mova's frames reader (`prag`), each text read on its own. Elamur Cosmic License.

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