Triple
T3123838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean‑François Gagné |
E65247
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | artificial intelligence expert |
C3390
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: artificial intelligence expert Context triple: [Jean‑François Gagné, instanceOf, artificial intelligence expert]
-
A.
machine learning researcher
chosen
A machine learning researcher is a specialist who develops, analyzes, and improves algorithms and models that enable computers to learn from data and make predictions or decisions.
-
B.
aerospace expert
An aerospace expert is a highly skilled professional with deep knowledge of aeronautics and astronautics who designs, analyzes, and optimizes aircraft, spacecraft, and related systems for performance, safety, and reliability.
-
C.
development expert
A development expert is a professional who applies specialized knowledge, tools, and methodologies to plan, implement, and optimize projects or programs that drive sustainable growth and improvement in a specific domain or region.
-
D.
art expert
An art expert is a specialist with deep knowledge of art history, techniques, styles, and market value who can analyze, interpret, and authenticate artworks.
-
E.
enterprise AI product
An enterprise AI product is a scalable, secure software solution that embeds artificial intelligence into business workflows to automate tasks, augment decision-making, and deliver measurable operational and strategic value across an organization.
- F. None of above.
Provenance (1 batch)
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_69ad8580c72481909672d37acf647893 |
completed | March 8, 2026, 2:19 p.m. |
Created at: March 8, 2026, 3:04 p.m.