Triple
T15361053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olivier Bousquet |
E367288
|
entity |
| Predicate | hasAcademicAdvisor |
P167
|
FINISHED |
| Object | Bernhard Schölkopf |
E1115575
|
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: Bernhard Schölkopf | Statement: [Olivier Bousquet, hasAcademicAdvisor, Bernhard Schölkopf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bernhard Schölkopf Context triple: [Olivier Bousquet, hasAcademicAdvisor, Bernhard Schölkopf]
-
A.
Bernhard Schölkopf
chosen
Bernhard Schölkopf is a leading German machine learning researcher known for his foundational work in kernel methods and causal inference, and for helping establish the Max Planck Institute for Intelligent Systems as a major center for AI research.
-
B.
Thore Graepel
Thore Graepel is a German computer scientist and machine learning researcher known for his work at DeepMind on game-playing AI systems and reinforcement learning.
-
C.
Max Welling
Max Welling is a prominent machine learning researcher known for foundational contributions to probabilistic deep learning and Bayesian inference, including co-developing variational autoencoders.
-
D.
Martin Riedmiller
Martin Riedmiller is a German computer scientist and pioneer in deep reinforcement learning, known for his influential work on neural-network-based control and contributions to landmark deep RL systems.
-
E.
Vladimir Vapnik
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.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e4607408190ab281a7f7a8012d3 |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff0b4a181c8190bffc1ac1a86e215d |
completed | May 9, 2026, 10:24 a.m. |
Created at: April 10, 2026, 3:18 a.m.