Triple
T4654882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TensorFlow Extended |
E102383
|
entity |
| Predicate | supportsOrchestrator |
P58238
|
FINISHED |
| Object | Vertex AI Pipelines |
E97118
|
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: Vertex AI Pipelines | Statement: [TensorFlow Extended, supportsOrchestrator, Vertex AI Pipelines]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vertex AI Pipelines Context triple: [TensorFlow Extended, supportsOrchestrator, Vertex AI Pipelines]
-
A.
Vertex AI
chosen
Vertex AI is Google Cloud’s unified machine learning platform for building, training, and deploying ML models at scale.
-
B.
NVIDIA AI Workflows
NVIDIA AI Workflows are pre-built, end-to-end AI pipelines from NVIDIA that streamline the development, deployment, and scaling of AI applications across common enterprise use cases.
-
C.
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.
-
D.
Google Cloud Dataflow
Google Cloud Dataflow is a fully managed service for developing and executing batch and streaming data processing pipelines, based on Apache Beam, within the Google Cloud ecosystem.
-
E.
Amazon SageMaker
Amazon SageMaker is a fully managed cloud service that enables developers and data scientists to build, train, and deploy machine learning models at scale.
- 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_69bd43d823288190952279faa0d1d066 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd6c3d1cb88190a42919dcbfe2568c |
completed | March 20, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfaef125c819097d79f25608302dc |
completed | March 21, 2026, 1:57 a.m. |
Created at: March 20, 2026, 1:14 p.m.