Triple

T9062800
Position Surface form Disambiguated ID Type / Status
Subject Marcus Hutter E217168 entity
Predicate authorOf P4244 FINISHED
Object Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability E774591 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: Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability | Statement: [Marcus Hutter, authorOf, Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability
Context triple: [Marcus Hutter, authorOf, Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability]
  • A. Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability chosen
    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.
  • B. Universal Intelligence: A Definition of Machine Intelligence
    "Universal Intelligence: A Definition of Machine Intelligence" is a foundational paper by Shane Legg (with Marcus Hutter) that formally defines and mathematically characterizes general machine intelligence using concepts from algorithmic information theory and reinforcement learning.
  • 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. 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. universal intelligence measure
    The universal intelligence measure is a formal, mathematical framework proposed to quantify and compare the general intelligence of agents across all possible environments.
  • 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_69d01798a79081909885a8e61bf04dc3 completed April 3, 2026, 7:40 p.m.
Created at: March 30, 2026, 7:11 p.m.