Triple
T5052802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Google Gemini |
E113825
|
entity |
| Predicate | usedInProduct |
P4614
|
FINISHED |
| Object | Google Cloud Vertex AI |
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: Google Cloud Vertex AI | Statement: [Google Gemini, usedInProduct, Google Cloud Vertex AI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Google Cloud Vertex AI Context triple: [Google Gemini, usedInProduct, Google Cloud Vertex AI]
-
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.
Google Cloud TPU V2
Google Cloud TPU V2 is a second-generation tensor processing unit offered as a cloud service by Google, designed to accelerate large-scale machine learning workloads such as deep neural network training and inference.
-
D.
Google Cloud TPU V3
Google Cloud TPU v3 is a high-performance, third-generation tensor processing unit offered on Google Cloud for accelerating large-scale machine learning and deep learning workloads.
-
E.
Kubeflow Pipelines
Kubeflow Pipelines is a platform for building, deploying, and managing end-to-end machine learning workflows on Kubernetes using containerized components.
- 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_69bd443aa1f88190abb992d138f2cf42 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd77cb8d2c8190a0f7c574a177091a |
completed | March 20, 2026, 4:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bea486b394819082ea80694843b29e |
completed | March 21, 2026, 2 p.m. |
Created at: March 20, 2026, 1:38 p.m.