IBM Match 360
E699691
IBM Match 360 is a master data management solution that unifies and reconciles customer and entity data from multiple sources to create a single, trusted view for analytics and operations.
All labels observed (1)
| Label | Occurrences |
|---|---|
| IBM Match 360 canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7937423 — 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: IBM Match 360 Context triple: [IBM Data and AI portfolio, hasComponent, IBM Match 360]
-
A.
Adobe Sensei
Adobe Sensei is Adobe’s artificial intelligence and machine learning platform that powers intelligent features and automation across its creative, document, and experience cloud products.
-
B.
Platform 7
Platform 7 is one of the passenger train platforms at Perth railway station in Perth, Western Australia.
-
C.
WATSON
WATSON is a close-up imaging camera on NASA’s Perseverance rover used to examine the fine details of Martian rocks and surface materials.
-
D.
Platform 5
Platform 5 is one of the passenger train platforms at Perth railway station in Perth, Western Australia.
-
E.
Platform 5
Platform 5 is one of the passenger train platforms at Cardiff Queen Street railway station in Cardiff, Wales.
- 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: IBM Match 360 Target entity description: IBM Match 360 is a master data management solution that unifies and reconciles customer and entity data from multiple sources to create a single, trusted view for analytics and operations.
-
A.
Adobe Sensei
Adobe Sensei is Adobe’s artificial intelligence and machine learning platform that powers intelligent features and automation across its creative, document, and experience cloud products.
-
B.
Platform 7
Platform 7 is one of the passenger train platforms at Perth railway station in Perth, Western Australia.
-
C.
WATSON
WATSON is a close-up imaging camera on NASA’s Perseverance rover used to examine the fine details of Martian rocks and surface materials.
-
D.
Platform 5
Platform 5 is one of the passenger platforms at Blackpool North railway station, serving trains on this major terminus in the seaside resort of Blackpool, England.
-
E.
Platform 5
Platform 5 is one of the passenger train platforms at Perth railway station in Perth, Western Australia.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
customer data platform component
ⓘ
master data management solution ⓘ software product ⓘ |
| deploymentModel |
cloud
ⓘ
containerized ⓘ |
| developer | IBM ⓘ |
| hasFeature |
REST APIs
NERFINISHED
ⓘ
audit and lineage tracking ⓘ data consolidation ⓘ data enrichment ⓘ data source onboarding ⓘ data stewardship UI ⓘ deterministic matching ⓘ entity resolution across sources ⓘ graph-based data model ⓘ match configuration tools ⓘ probabilistic matching ⓘ relationship discovery ⓘ search and query interface ⓘ survivorship rules ⓘ |
| hasPurpose |
customer data unification
ⓘ
entity resolution ⓘ master data management ⓘ single customer view creation ⓘ |
| integratesWith |
IBM Cloud Pak for Data services
NERFINISHED
ⓘ
data lakes ⓘ data warehouses ⓘ operational applications ⓘ |
| partOf |
IBM Cloud Pak for Data
NERFINISHED
ⓘ
IBM Data and AI portfolio NERFINISHED ⓘ |
| provides |
single trusted view of customer data
ⓘ
single trusted view of entity data ⓘ |
| runsOn | IBM Cloud Pak for Data platform NERFINISHED ⓘ |
| supportsDomain |
customer data
ⓘ
entity data ⓘ party data ⓘ |
| supportsUseCase |
customer 360 analytics
ⓘ
customer segmentation ⓘ data quality improvement ⓘ data stewardship ⓘ identity resolution ⓘ operational customer view ⓘ regulatory compliance reporting ⓘ |
| targetUser |
business analysts
ⓘ
data architects ⓘ data engineers ⓘ data stewards ⓘ |
| usedFor |
analytics
ⓘ
customer experience optimization ⓘ operational decisioning ⓘ |
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: IBM Match 360 Description of subject: IBM Match 360 is a master data management solution that unifies and reconciles customer and entity data from multiple sources to create a single, trusted view for analytics and operations.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.