Triple
T9674965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MXNet |
E234123
|
entity |
| Predicate | integratedInto |
P77
|
FINISHED |
| Object | Amazon SageMaker |
E293756
|
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 SageMaker | Statement: [MXNet, integratedInto, Amazon SageMaker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amazon SageMaker Context triple: [MXNet, integratedInto, Amazon SageMaker]
-
A.
Amazon SageMaker
chosen
Amazon SageMaker is a fully managed cloud service that enables developers and data scientists to build, train, and deploy machine learning models at scale.
-
B.
Landing AI
Landing AI is a technology company focused on making artificial intelligence accessible to traditional industries by helping them build and deploy practical AI solutions, particularly in manufacturing and computer vision.
-
C.
Azure Machine Learning
Azure Machine Learning is a cloud-based service from Microsoft for building, training, deploying, and managing machine learning models at scale on Azure.
-
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.
Turi Create
Turi Create is an open-source Python library from Apple that simplifies building, training, and deploying machine learning models, especially for use with Apple’s Core ML framework.
- 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_69ca848f55e48190b3f67252571c3d45 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9c6d6dd48190a77c486337a58cb6 |
completed | April 1, 2026, 10:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d18a2d030c8190ada52e855bc9afc8 |
completed | April 4, 2026, 10:01 p.m. |
Created at: March 30, 2026, 8:15 p.m.