Triple
T7874750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adam: A Method for Stochastic Optimization |
E182822
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Jimmy Ba |
E34729
|
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: Jimmy Ba | Statement: [Adam: A Method for Stochastic Optimization, author, Jimmy Ba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jimmy Ba Context triple: [Adam: A Method for Stochastic Optimization, author, Jimmy Ba]
-
A.
Jimmy Ba
chosen
Jimmy Ba is a prominent machine learning researcher known for his work on deep learning optimization methods such as the Adam optimizer.
-
B.
Jimmy Baio
Jimmy Baio is an American former child actor best known for his role as Billy Tate on the sitcom "Soap" in the late 1970s and early 1980s.
-
C.
Johnny U
Johnny U is the legendary Hall of Fame NFL quarterback Johnny Unitas, renowned for revolutionizing the modern passing game with the Baltimore Colts.
-
D.
Jimmy Slyde
Jimmy Slyde was an influential American tap dancer renowned for his smooth, gliding style and improvisational jazz-infused performances.
-
E.
Booger McFarland
Booger McFarland is a former NFL defensive tackle who became a prominent American football television analyst and color commentator.
- 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_69ca828a17248190b46defe758bc5ad3 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb39a961188190b2f12f8fe5d66641 |
completed | March 31, 2026, 3:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbdf9535c48190a73653a773553d01 |
completed | March 31, 2026, 2:52 p.m. |
Created at: March 30, 2026, 4:56 p.m.