PromQL
E699840
PromQL is the powerful, flexible query language used to retrieve and aggregate time-series metrics in Prometheus-based monitoring systems.
All labels observed (1)
| Label | Occurrences |
|---|---|
| PromQL canonical | 3 |
How this entity was disambiguated
This entity first appeared as the object of triple T7939706 — 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: PromQL Context triple: [Prometheus, supports, PromQL]
-
A.
Datadog
Datadog is a cloud-based monitoring and security platform that provides observability into applications, infrastructure, logs, and metrics for modern DevOps and IT teams.
-
B.
kobs
Kobs is a genus of moths within the subfamily Reduncinae, a group of noctuid moths.
-
C.
TSDB
TSDB (Terrorist Screening Database) is the U.S. government’s central consolidated watchlist of known or suspected terrorists used for screening and security purposes.
-
D.
Sumo Logic
Sumo Logic is a cloud-native machine data analytics and log management platform that helps organizations monitor, troubleshoot, and secure their applications and infrastructure in real time.
-
E.
Splunk
Splunk is a data analytics platform that specializes in collecting, indexing, and analyzing machine-generated data for monitoring, security, and operational intelligence.
- 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: PromQL Target entity description: PromQL is the powerful, flexible query language used to retrieve and aggregate time-series metrics in Prometheus-based monitoring systems.
-
A.
VividCortex
VividCortex is a database performance monitoring and analytics platform designed to help teams optimize and troubleshoot production database workloads in real time.
-
B.
Datadog
Datadog is a cloud-based monitoring and security platform that provides observability into applications, infrastructure, logs, and metrics for modern DevOps and IT teams.
-
C.
kobs
Kobs is a genus of moths within the subfamily Reduncinae, a group of noctuid moths.
-
D.
TSDB
TSDB (Terrorist Screening Database) is the U.S. government’s central consolidated watchlist of known or suspected terrorists used for screening and security purposes.
-
E.
Sumo Logic
Sumo Logic is a cloud-native machine data analytics and log management platform that helps organizations monitor, troubleshoot, and secure their applications and infrastructure in real time.
- F. None of above. chosen
Statements (56)
| Predicate | Object |
|---|---|
| instanceOf |
query language
ⓘ
software component ⓘ time series query language ⓘ |
| developedBy |
Prometheus project
ⓘ
linked to:
Prometheus
|
| hasConcept |
instant vector
ⓘ
range vector ⓘ scalar ⓘ string literal ⓘ |
| hasFunction |
absent
ⓘ
histogram_quantile ⓘ increase ⓘ irate ⓘ label_join ⓘ label_replace ⓘ rate ⓘ timestamp ⓘ |
| hasOperator |
avg
ⓘ
bottomk ⓘ count ⓘ max ⓘ min ⓘ sort ⓘ sort_desc ⓘ sum ⓘ topk ⓘ |
| influenced |
LogQL
ⓘ
Prometheus-compatible query languages ⓘ |
| partOf |
Prometheus ecosystem
ⓘ
linked to:
Prometheus
|
| programmingParadigm | declarative ⓘ |
| supports |
@ modifier for explicit timestamps
ⓘ
aggregation operators ⓘ arithmetic operators ⓘ comparison operators ⓘ functions ⓘ histogram operations ⓘ instant queries ⓘ label aggregation ⓘ label-based selection ⓘ logical operators ⓘ offset modifier ⓘ range queries ⓘ rate calculations ⓘ regular expression matching on labels ⓘ subqueries ⓘ time aggregation ⓘ time range selection ⓘ vector matching ⓘ |
| usedBy | Prometheus ⓘ |
| usedFor |
alerting rules
ⓘ
dashboard queries ⓘ metrics aggregation ⓘ monitoring and observability ⓘ querying time-series data ⓘ |
| usedIn |
Grafana dashboards
ⓘ
Prometheus alerting rules ⓘ Prometheus recording rules ⓘ |
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: PromQL Description of subject: PromQL is the powerful, flexible query language used to retrieve and aggregate time-series metrics in Prometheus-based monitoring systems.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.