Triple
T9062923
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ray Solomonoff |
E217171
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Solomonoff |
E217171
|
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: Solomonoff | Statement: [Ray Solomonoff, familyName, Solomonoff]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Solomonoff Context triple: [Ray Solomonoff, familyName, Solomonoff]
-
A.
Solomonoff induction
Solomonoff induction is a formal theory of universal prediction that combines algorithmic information theory and Bayesian reasoning to define an idealized, incomputable method for inferring future data from past observations.
-
B.
Ray Solomonoff
chosen
Ray Solomonoff was a pioneering mathematician and one of the founders of algorithmic information theory, best known for introducing Solomonoff induction as a formal theory of universal prediction and inductive inference.
-
C.
Marcus Hutter
Marcus Hutter is a computer scientist known for his foundational work in universal artificial intelligence and the development of the AIXI model of optimal decision-making.
-
D.
AIXI model
The AIXI model is a theoretical framework for an idealized, maximally intelligent reinforcement learning agent that combines Solomonoff induction with sequential decision theory.
-
E.
Kolmogorov complexity
Kolmogorov complexity is a measure of the amount of information in an object, defined as the length of the shortest computer program that can produce it.
- 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_69ca83d4425481909a319dab847724ec |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc94b9f28481909e20366b0e3d14aa |
completed | April 1, 2026, 3:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d054413cd881908bd4aca6a69ef663 |
completed | April 3, 2026, 11:58 p.m. |
Created at: March 30, 2026, 7:11 p.m.