Triple
T7939706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prometheus |
E184359
|
entity |
| Predicate | supports |
P516
|
FINISHED |
| Object |
PromQL
PromQL is the powerful, flexible query language used to retrieve and aggregate time-series metrics in Prometheus-based monitoring systems.
|
E699840
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: PromQL | Statement: [Prometheus, supports, PromQL]
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.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: PromQL Triple: [Prometheus, supports, PromQL]
Generated description
PromQL is the powerful, flexible query language used to retrieve and aggregate time-series metrics in Prometheus-based monitoring systems.
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
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca8290c21c8190906a5ca6fe2b03c4 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3b0983388190a77e8d5d899c5130 |
completed | March 31, 2026, 3:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5c0e868481908748d340244ea8ea |
completed | March 31, 2026, 5:30 a.m. |
| NEDg | Description generation | batch_69cb7634f4dc8190b5e537f24bccd651 |
completed | March 31, 2026, 7:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cbb67e77a48190b93c6ba61becfac4 |
completed | March 31, 2026, 11:56 a.m. |
Created at: March 30, 2026, 5:08 p.m.