Triple
T15360997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vladimir Vapnik |
E367287
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object | VC dimension |
E1115572
|
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: VC dimension | Statement: [Vladimir Vapnik, knownFor, VC dimension]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: VC dimension Context triple: [Vladimir Vapnik, knownFor, VC dimension]
-
A.
VC dimension
chosen
VC dimension is a fundamental measure of the capacity or complexity of a hypothesis class in statistical learning theory, indicating how well it can fit diverse datasets.
-
B.
Probably Approximately Correct learning (PAC learning)
Probably Approximately Correct (PAC) learning is a foundational framework in computational learning theory that formalizes what it means for an algorithm to efficiently learn a concept from examples with high probability and small error.
-
C.
Hausdorff dimension
The Hausdorff dimension is a mathematical concept in fractal geometry and measure theory that generalizes the notion of dimension to capture the scaling complexity of irregular sets.
-
D.
Lyapunov dimension
The Lyapunov dimension is a fractal dimension used in dynamical systems theory to quantify the effective number of degrees of freedom of a chaotic attractor based on its Lyapunov exponents.
-
E.
Fisher's linear discriminant
Fisher's linear discriminant is a classic statistical technique for dimensionality reduction and classification that projects data onto a line to maximize separation between classes.
- 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.