WLN
E556362
WLN is the stock ticker symbol for Worldline, a major European provider of payment and transactional services.
All labels observed (1)
| Label | Occurrences |
|---|---|
| WLN canonical | 3 |
How this entity was disambiguated
This entity first appeared as the object of triple T5934252 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WLN Context triple: [Worldline, tickerSymbol, WLN]
-
A.
WLWT
WLWT is a Cincinnati-based television station that serves as a major local NBC affiliate in the United States.
-
B.
WTN
WTN is the IATA airport code for RAF Waddington, a Royal Air Force station in Lincolnshire, England.
-
C.
WLBZ
WLBZ is a television station serving the Bangor, Maine market as a primary network affiliate.
-
D.
WNL
WNL is the National Rail station code for Whinhill railway station in Inverclyde, Scotland.
-
E.
WNLO
WNLO is a major Chinese research institute specializing in optoelectronics and photonics, based in Wuhan.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: WLN Target entity description: WLN is the stock ticker symbol for Worldline, a major European provider of payment and transactional services.
-
A.
WLWT
WLWT is a Cincinnati-based television station that serves as a major local NBC affiliate in the United States.
-
B.
WTN
WTN is the IATA airport code for RAF Waddington, a Royal Air Force station in Lincolnshire, England.
-
C.
WLBZ
WLBZ is a television station serving the Bangor, Maine market as a primary network affiliate.
-
D.
WNL
WNL is the National Rail station code for Whinhill railway station in Inverclyde, Scotland.
-
E.
WNLO
WNLO is a major Chinese research institute specializing in optoelectronics and photonics, based in Wuhan.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
financial technology company
ⓘ
payment services provider ⓘ public company ⓘ stock ticker symbol ⓘ |
| continent | Europe ⓘ |
| country | France ⓘ |
| countryOfExchange | France ⓘ |
| currencyOfListing | EUR ⓘ |
| exchange |
Euronext Paris
ⓘ
linked to:
Euronext
|
| hasService |
card issuing processing
ⓘ
digital services ⓘ fraud risk management services ⓘ in-store payments ⓘ merchant acquiring ⓘ omnichannel payment solutions ⓘ online payments ⓘ payment processing ⓘ terminal services ⓘ transaction processing ⓘ |
| hasWebsite | https://worldline.com ⓘ |
| headquartersLocation | Bezons, France ⓘ |
| industry |
financial services
ⓘ
information technology services ⓘ payments ⓘ transactional services ⓘ |
| ISIN | FR0011981968 ⓘ |
| languageOfHeadquarters | French ⓘ |
| legalForm | société anonyme ⓘ |
| listedOn |
Euronext Paris
ⓘ
linked to:
Euronext
|
| majorShareholderHistory | Atos SE ⓘ |
| market |
Euronext Paris regulated market
ⓘ
linked to:
Euronext
|
| origin | spin-off from Atos payment activities ⓘ |
| providesTo |
acquirers
ⓘ
banks ⓘ merchants ⓘ public sector ⓘ |
| region | Europe ⓘ |
| regionServed |
Europe
ⓘ
global ⓘ |
| represents |
Worldline SA
ⓘ
linked to:
Worldline
|
| securityType | equity ⓘ |
| segment |
financial services
ⓘ
merchant services ⓘ mobility and e-transactional services ⓘ |
| tickerSymbol | WLN ⓘ |
| tradedAs | WLN ⓘ |
| underlyingCompany |
Worldline SA
ⓘ
linked to:
Worldline
|
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: WLN Description of subject: WLN is the stock ticker symbol for Worldline, a major European provider of payment and transactional services.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject linked to:
WLN