Triple
T14721074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NeurIPS Test of Time Award |
E345813
|
entity |
| Predicate | notableRecipient |
P108
|
FINISHED |
| Object | Vladimir Vapnik |
E367287
|
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: Vladimir Vapnik | Statement: [NeurIPS Test of Time Award, notableRecipient, Vladimir Vapnik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vladimir Vapnik Context triple: [NeurIPS Test of Time Award, notableRecipient, Vladimir Vapnik]
-
A.
Vladimir Vapnik
chosen
Vladimir Vapnik is a pioneering computer scientist and statistician best known as a co-inventor of support vector machines and a founder of statistical learning theory.
-
B.
Zvi Kohavi
Zvi Kohavi was a computer scientist and academic known for his influential work and textbooks in automata theory, switching and finite automata, and the mathematical foundations of computation.
-
C.
Léon Bottou
Léon Bottou is a French computer scientist known for his influential work in machine learning and neural networks, including key contributions to the development of the LeNet convolutional network.
-
D.
Gregory Piatetsky-Shapiro
Gregory Piatetsky-Shapiro is a pioneering computer scientist and data mining expert best known as the founder of the KDD (Knowledge Discovery and Data Mining) conferences and the KDnuggets data science community.
-
E.
Emanuel Parzen
Emanuel Parzen was an American statistician renowned for pioneering kernel density estimation, particularly through the development of the Parzen window method.
- 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_69d822e5911c8190ba589f957dbd9ba7 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec25d56fc8190871873ca55d49272 |
completed | April 14, 2026, 10:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdf0957bb081908f1f382f3be8ec20 |
completed | May 8, 2026, 2:17 p.m. |
Created at: April 10, 2026, 1:29 a.m.