Triple
T12323307
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amazon MQ |
E293771
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | Amazon EKS |
E184364
|
NE FINISHED |
How this triple was built (2 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: Amazon EKS | Statement: [Amazon MQ, integratesWith, Amazon EKS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amazon EKS Context triple: [Amazon MQ, integratesWith, Amazon EKS]
-
A.
Amazon Elastic Kubernetes Service
chosen
Amazon Elastic Kubernetes Service (Amazon EKS) is a cloud-based, fully managed service from AWS for running and scaling containerized applications using Kubernetes.
-
B.
Amazon ECS
Amazon ECS is a fully managed container orchestration service that lets users run, scale, and secure Docker containers on AWS infrastructure.
-
C.
Azure Kubernetes Service
Azure Kubernetes Service is a managed container orchestration platform that simplifies deploying, scaling, and operating Kubernetes clusters in the Microsoft Azure cloud.
-
D.
AWS Fargate
AWS Fargate is a serverless compute engine for containers that lets users run Docker-based applications on AWS without managing underlying servers or clusters.
-
E.
Amazon MSK
Amazon MSK is a fully managed Apache Kafka service from AWS that simplifies setting up, scaling, and operating Kafka clusters for streaming data applications.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6ab6ae0dc8190b1522a9c1c55c114 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f4d7dac81909ff10e64e229ef33 |
completed | April 10, 2026, 6:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64b8ed1dc81908a0066d7cbfda086 |
completed | May 2, 2026, 7:07 p.m. |
Created at: April 8, 2026, 9:53 p.m.