---
title: In 1854 the doctor John Snow mapped cholera cases in a London street… — Olympiad: cause and effect chains ★★★ · tree and pragmatics
description: In 1854 the doctor John Snow mapped cholera cases in a London street and showed they gathered round one water pump; after the «Great Stink» of the Thames in 1858, London built big new sewers. Who acts: doctor, people, London. A sentence of the lesson «Olympiad: cause and effect chains ★★★» of the hi
---

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# Olympiad: cause and effect chains ★★★, sentence 17

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In 1854 the doctor John Snow mapped cholera cases in a London street and showed they gathered round one water pump; after the «Great Stink» of the Thames in 1858, London built big new sewers.

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**Who acts:** doctor, people, London · **What happens:** map, show, gather, build · **To what, where:** cholera cases, 1854, one water pump, big new sewers · **Speech:** someone speaks here

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This is a sentence of the lesson «Olympiad: cause and effect chains ★★★» of the history course of Mova's primary school. The page gives its syntax tree — a dependency tree in the style of Universal Dependencies — and its pragmatics clause by clause: who acts (doctor, people, London), what happens, when and where, as Mova's frames read it from the tree.

## The tree {#tree}

```conllu
# sent_id = school:history:086-olympiad-cause-and-effect-chains.lesson:17
# text = In 1854 the doctor John Snow mapped cholera cases in a London street and showed they gathered round one water pump; after the «Great Stink» of the Thames in 1858, London built big new sewers.
# source = school lesson (ours)
# course = history
# lesson = 086-olympiad-cause-and-effect-chains.lesson
# annotation = Mova annot: Rust (en) → Haiku 5.5 (medium, Batches API) patches; gates UD 2.18; agreed by treeagree rules (Stanza, Trankit, spaCy trf): R1-cc-next ×1, R5-strong ×1, R7-rel-agree ×1, R8-pos-agree ×1
1	In	in	ADP	IN	_	2	case	_	_
2	1854	1854	NUM	CD	NumForm=Digit|NumType=Card	7	obl	_	_
3	the	the	DET	DT	Definite=Def|PronType=Art	4	det	_	_
4	doctor	doctor	NOUN	NN	Number=Sing	7	nsubj	_	_
5	John	John	PROPN	NNP	Number=Sing	6	compound	_	_
6	Snow	snow	NOUN	NN	Number=Sing	4	appos	_	_
7	mapped	map	VERB	VBD	Mood=Ind|Number=Sing|Person=3|Tense=Past|VerbForm=Fin	36	parataxis	_	_
8	cholera	cholera	NOUN	NN	Number=Sing	9	compound	_	_
9	cases	case	NOUN	NNS	Number=Plur	7	obj	_	_
10	in	in	ADP	IN	_	13	case	_	_
11	a	a	DET	DT	Definite=Ind|PronType=Art	13	det	_	_
12	London	London	PROPN	NNP	Number=Sing	13	compound	_	_
13	street	street	NOUN	NN	Number=Sing	9	nmod	_	_
14	and	and	CCONJ	CC	_	15	cc	_	Agree=R1-cc-next|Haiku=7:cc
15	showed	show	VERB	VBD	Mood=Ind|Number=Sing|Person=3|Tense=Past|VerbForm=Fin	7	conj	_	_
16	they	they	PRON	PRP	Case=Nom|Number=Plur|Person=3|PronType=Prs	17	nsubj	_	_
17	gathered	gather	VERB	VBD	Mood=Ind|Number=Plur|Person=3|Tense=Past|VerbForm=Fin	15	ccomp	_	_
18	round	round	ADV	RB	_	17	advmod	_	_
19	one	one	NUM	CD	NumForm=Word|NumType=Card	21	nummod	_	_
20	water	water	NOUN	NN	Number=Sing	21	compound	_	_
21	pump	pump	NOUN	NN	Number=Sing	17	obl	_	Agree=R7-rel-agree|Haiku=17:obj
22	;	;	PUNCT	,	_	36	punct	_	_
23	after	after	ADP	IN	_	27	case	_	_
24	the	the	DET	DT	Definite=Def|PronType=Art	27	det	_	_
25	«	«	PUNCT	``	_	27	punct	_	_
26	Great	great	ADJ	JJ	Degree=Pos	27	amod	_	_
27	Stink	stink	PROPN	NNP	Number=Sing	36	obl	_	Agree=R8-pos-agree|Haiku=36:obl|HaikuPos=NOUN:NN:Number=Sing
28	»	»	PUNCT	''	_	27	punct	_	_
29	of	of	ADP	IN	_	31	case	_	_
30	the	the	DET	DT	Definite=Def|PronType=Art	31	det	_	_
31	Thames	Thames	PROPN	NNP	Number=Sing	27	nmod	_	_
32	in	in	ADP	IN	_	33	case	_	_
33	1858	1858	NUM	CD	NumForm=Digit|NumType=Card	27	nmod	_	Agree=R5-strong|Haiku=36:obl
34	,	,	PUNCT	,	_	36	punct	_	_
35	London	London	PROPN	NNP	Number=Sing	36	nsubj	_	_
36	built	build	VERB	VBD	Mood=Ind|Number=Sing|Person=3|Tense=Past|VerbForm=Fin	0	root	_	_
37	big	big	ADJ	JJ	Degree=Pos	39	amod	_	_
38	new	new	ADJ	JJ	Degree=Pos	39	amod	_	_
39	sewers	sewer	NOUN	NNS	Number=Plur	36	obj	_	_
40	.	.	PUNCT	.	_	36	punct	_	_
```

## Pragmatics {#pragmatics}

<!-- .note -->
No stored speech act for this sentence.

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| # | verb | tense, mood | who | what, whom | to whom | where | when | other | roles by |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | **map** | past | the doctor | cholera cases |  | 1854 |  | in a London street | tree rules |
| 2 | **show** | past | the doctor *not said here* |  |  |  |  |  | vn transfer\_mesg-37.1.1-1-1 «NP V how S»: agent=Agent clause=Topic |
| 3 | **gather** | past | people | one water pump |  |  |  | round *property* | vn herd-47.5.2 «NP V»: agent=Theme |
| 4 | **build** | past | London | big new sewers |  |  | after the Great Stink of the Thames in 1858 |  | vn build-26.1-1 «NP V NP»: agent=Agent sewer=Product |

<!-- .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.

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**Licence: [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)**, as the whole of Mova corpus 0.4, of which this treebank (the agreed school, `mova-school-agreed-en.conllu`) is part. The lessons were written for Mova's primary school. Sentence `school:history:086-olympiad-cause-and-effect-chains.lesson:17`.

**The tree:** Mova annot: Rust (en) → Haiku 5.5 (medium, Batches API) patches; gates UD 2.18; agreed by treeagree rules (Stanza, Trankit, spaCy trf): R1-cc-next ×1, R5-strong ×1, R7-rel-agree ×1, R8-pos-agree ×1. **The frames** are read from the tree by Mova's frames reader (`prag`) when this page is built, the lesson read in order, so a pronoun may be resolved from an earlier sentence.

See the [release 0.4](https://md.mova.elamur.ai/free/en/releases/0.4/).

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- ← previous: [Olympiad: cause and effect chains ★★★, 16](https://md.mova.elamur.ai/free/en/corpora/school/history/86-olympiad-cause-and-effect-chains/16/)
- next →: [Olympiad: cause and effect chains ★★★, 18](https://md.mova.elamur.ai/free/en/corpora/school/history/86-olympiad-cause-and-effect-chains/18/)
- ↑ back to [the lesson](https://md.mova.elamur.ai/free/en/corpora/school/history/86-olympiad-cause-and-effect-chains/)

