Triple
T8482818
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volodymyr Mnih |
E200558
|
entity |
| Predicate | coAuthorWith |
P398
|
FINISHED |
| Object | Alex Graves |
E200559
|
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: Alex Graves | Statement: [Volodymyr Mnih, coAuthorWith, Alex Graves]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alex Graves Context triple: [Volodymyr Mnih, coAuthorWith, Alex Graves]
-
A.
Alex Graves
chosen
Alex Graves is a computer scientist and machine learning researcher known for his influential work on recurrent neural networks, deep reinforcement learning, and sequence modeling.
-
B.
Alex Convery
Alex Convery is a screenwriter best known for writing the script for the 2023 sports drama film "Air," which chronicles Nike's pursuit of Michael Jordan.
-
C.
Alex Flanagan
Alex Flanagan is an American sportscaster and sideline reporter known for her work covering major NFL games and other high-profile sporting events on national television.
-
D.
Alex Datcher
Alex Datcher is an American actress best known for her role as a flight attendant alongside Wesley Snipes in the 1992 action film "Passenger 57."
-
E.
Matthew Rhodes
Matthew Rhodes is a film producer known for his work on projects such as the psychological thriller "The Voices."
- 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_69ca831b17988190a1f3f3413d57b820 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe53845e881909eeb32863c7aa942 |
completed | March 31, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce3a2b2e9081909f19712946c6ec20 |
completed | April 2, 2026, 9:43 a.m. |
Created at: March 30, 2026, 6:12 p.m.