Triple
T18800810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AWS CDK |
E459748
|
entity |
| Predicate | supportsService |
P203
|
FINISHED |
| Object | AWS Batch |
—
|
NE NERFINISHED |
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: AWS Batch | Statement: [AWS CDK, supportsService, AWS Batch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AWS Batch Context triple: [AWS CDK, supportsService, AWS Batch]
-
A.
AWS Batch
chosen
AWS Batch is a fully managed AWS service that efficiently runs and scales batch computing workloads on AWS infrastructure without requiring users to manage servers or job schedulers.
-
B.
Azure Batch
Azure Batch is a Microsoft cloud service that enables large-scale parallel and high-performance computing (HPC) workloads by automatically managing and scaling compute resources.
-
C.
Amazon ECS
Amazon ECS is a fully managed container orchestration service that lets users run, scale, and secure Docker containers on AWS infrastructure.
-
D.
Amazon EMR
Amazon EMR is a managed big data platform on AWS that simplifies running large-scale data processing frameworks like Apache Hadoop and Spark on elastic cloud clusters.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a02332d88190b68feea7f2f86d06 |
completed | April 20, 2026, 3:40 a.m. |
Created at: April 10, 2026, 11:53 a.m.