Triple
T4279702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vertex AI |
E97118
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | Vertex AI Feature Store |
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 Feature Store | Statement: [Vertex AI, hasComponent, Vertex AI Feature Store]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vertex AI Feature Store Context triple: [Vertex AI, hasComponent, Vertex AI Feature Store]
-
A.
Vertex AI
chosen
Vertex AI is Google Cloud’s unified machine learning platform for building, training, and deploying ML 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.
Business Intelligence Development Studio
Business Intelligence Development Studio was Microsoft's former integrated development environment for creating SQL Server business intelligence solutions, including SSIS, SSAS, and SSRS projects.
-
D.
Snowflake Data Cloud
Snowflake Data Cloud is a cloud-native data platform that enables organizations to store, integrate, and analyze data at scale across multiple clouds with a unified, fully managed service.
-
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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b350367da48190b735deef9b5d2d2e |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5b7b708b481908c1683741f84ee55 |
completed | March 14, 2026, 7:32 p.m. |
Created at: March 12, 2026, 11:07 p.m.