Triple
T9062837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Universal Intelligence: A Definition of Machine Intelligence |
E217169
|
entity |
| Predicate | usesConcept |
P531
|
FINISHED |
| Object | Kolmogorov complexity |
E183589
|
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: Kolmogorov complexity | Statement: [Universal Intelligence: A Definition of Machine Intelligence, usesConcept, Kolmogorov complexity]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kolmogorov complexity Context triple: [Universal Intelligence: A Definition of Machine Intelligence, usesConcept, Kolmogorov complexity]
-
A.
Kolmogorov complexity
chosen
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.
-
B.
algorithmic information theory
Algorithmic information theory is a branch of theoretical computer science and mathematics that studies the complexity and information content of objects using concepts like Kolmogorov complexity and randomness.
-
C.
Blum complexity measures
Blum complexity measures are a formal framework in computational complexity theory that rigorously define and compare the resource usage (such as time or space) of algorithms via axiomatic conditions.
-
D.
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.
-
E.
Martin-Löf randomness
Martin-Löf randomness is a rigorous mathematical notion of randomness for infinite binary sequences, defined via effectively null sets and closely connected to algorithmic information 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_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_69d01798a79081909885a8e61bf04dc3 |
completed | April 3, 2026, 7:40 p.m. |
Created at: March 30, 2026, 7:11 p.m.