Triple
T18800807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AWS CDK |
E459748
|
entity |
| Predicate | supportsService |
P203
|
FINISHED |
| Object | Amazon MQ |
—
|
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: Amazon MQ | Statement: [AWS CDK, supportsService, Amazon MQ]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amazon MQ Context triple: [AWS CDK, supportsService, Amazon MQ]
-
A.
Amazon MQ
chosen
Amazon MQ is a managed message broker service that simplifies setting up and operating popular open-source message brokers like Apache ActiveMQ and RabbitMQ in the cloud.
-
B.
Amazon SQS
Amazon SQS is a fully managed message queuing service that enables decoupled, scalable communication between distributed application components in the cloud.
-
C.
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.
-
D.
RabbitMQ
RabbitMQ is an open-source message broker that implements the Advanced Message Queuing Protocol (AMQP) to enable reliable, scalable communication between distributed applications and services.
-
E.
Amazon Kinesis
Amazon Kinesis is a fully managed AWS service for real-time collection, processing, and analysis of streaming data at scale.
- 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.