Triple
T18724069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ashish Vaswani |
E457851
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Adept AI Labs |
—
|
NE NERFINISHED |
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: Adept AI Labs | Statement: [Ashish Vaswani, employer, Adept AI Labs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Adept AI Labs Context triple: [Ashish Vaswani, employer, Adept AI Labs]
-
A.
Adept AI
chosen
Adept AI is an artificial intelligence research and product company focused on building AI agents that can use existing software tools to perform complex tasks for users.
-
B.
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.
-
C.
AWI Labs
AWI Labs is a technology company established by entrepreneur Anthony Wood, best known as the founder of Roku.
-
D.
Garrett AiResearch
Garrett AiResearch was an American aerospace company best known for designing and manufacturing turboprop and turbojet engines and related aircraft systems.
-
E.
Einstein AI
Einstein AI is Salesforce’s integrated artificial intelligence platform that powers predictive analytics, automation, and intelligent insights across its CRM ecosystem.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d393ba9c8190a8b03b04ddbb0a09 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e56abcfc048190a01dee959e768768 |
completed | April 19, 2026, 11:52 p.m. |
Created at: April 10, 2026, 11:50 a.m.