Triple
T9062812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marcus Hutter |
E217168
|
entity |
| Predicate | hasConcept |
P531
|
FINISHED |
| Object | AIXI-tl |
E774590
|
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: AIXI-tl | Statement: [Marcus Hutter, hasConcept, AIXI-tl]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AIXI-tl Context triple: [Marcus Hutter, hasConcept, AIXI-tl]
-
A.
AIXI model
chosen
The AIXI model is a theoretical framework for an idealized, maximally intelligent reinforcement learning agent that combines Solomonoff induction with sequential decision theory.
-
B.
Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability is a foundational monograph by Marcus Hutter that rigorously develops a formal, mathematical theory of general artificial intelligence based on algorithmic information theory and optimal sequential decision-making.
-
C.
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.
-
D.
Adept AI
Adept AI is an artificial intelligence research and product company focused on building AI agents that can use existing software tools to perform complex tasks for users.
-
E.
MuZero
MuZero is a DeepMind reinforcement learning algorithm that learns to plan and master complex games like Go, chess, and Atari without being given the rules in advance.
- 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_69cc7ecd352c8190a744579209b2e535 |
completed | April 1, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d02fe00980819082571eccb608d605 |
completed | April 3, 2026, 9:23 p.m. |
Created at: March 30, 2026, 7:11 p.m.