Triple
T1634168
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Google Cloud |
E35325
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | 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: Vertex AI | Statement: [Google Cloud, hasComponent, Vertex AI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vertex AI Context triple: [Google Cloud, hasComponent, 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.
Einstein AI
Einstein AI is Salesforce’s integrated artificial intelligence platform that powers predictive analytics, automation, and intelligent insights across its CRM ecosystem.
-
C.
Meta AI
Meta AI is Meta Platforms’ artificial intelligence division, responsible for developing large-scale AI models, research, and consumer-facing tools like the Meta AI assistant integrated across its apps and services.
-
D.
Element AI
Element AI was a Montreal-based artificial intelligence company and research lab known for developing enterprise AI solutions and advancing deep learning research.
-
E.
Azure Cognitive Services
Azure Cognitive Services is a suite of cloud-based AI APIs and tools that enable developers to add capabilities like vision, speech, language understanding, and decision-making to their applications without needing deep machine learning expertise.
- 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_69a886036bc081909ff5de16dbe5e8ea |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a909f86abc8190b0b81310dcd7feed |
completed | March 5, 2026, 4:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad58d9db5c819081408977834ad606 |
completed | March 8, 2026, 11:09 a.m. |
Created at: March 4, 2026, 7:28 p.m.