Triple
T472312
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Element AI |
E8584
|
entity |
| Predicate | employerOf |
P7
|
FINISHED |
| Object | Jean‑François Gagné |
E65247
|
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: Jean‑François Gagné | Statement: [Element AI, employerOf, Jean‑François Gagné]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jean‑François Gagné Context triple: [Element AI, employerOf, Jean‑François Gagné]
-
A.
Jean‑François Gagné
chosen
Jean‑François Gagné is a Canadian entrepreneur and artificial intelligence expert best known as a co-founder and former CEO of the AI company Element AI.
-
B.
Philippe Beaudoin
Philippe Beaudoin is a Canadian computer scientist and entrepreneur known for co-founding the artificial intelligence company Element AI.
-
C.
Rigaud Benoit
Rigaud Benoit was a prominent Haitian painter associated with the mid-20th-century Haitian art movement, known for his vivid, symbolic depictions of Haitian life and spirituality.
-
D.
Bertrand Fagalde
Bertrand Fagalde was a French admiral best known for his leadership of French naval forces during the Battle of Dunkirk in World War II.
-
E.
Robert Fraisse
Robert Fraisse is a French cinematographer known for his visually striking work on international films, including major war dramas and action features.
- 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_69a2e7f3aeb48190a19453e3a043f486 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eff24108819092fdb85019ec4089 |
completed | Feb. 28, 2026, 1:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4b8a96a188190b7a6be463de3d736 |
completed | March 1, 2026, 10:07 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.