Triple
T7666849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Computability Theory |
E173643
|
entity |
| Predicate | fieldOfStudy |
P3
|
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: [Computability Theory, fieldOfStudy, Kolmogorov complexity]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kolmogorov complexity Context triple: [Computability Theory, fieldOfStudy, 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.
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.
-
C.
Computability Theory
Computability Theory is a branch of theoretical computer science and mathematical logic that studies which problems can be solved by algorithms and how efficiently they can be computed.
-
D.
Mathematical Foundations of Information Theory
Mathematical Foundations of Information Theory is a seminal monograph by Aleksandr Khinchin that rigorously develops the probabilistic and mathematical basis of Shannon’s information theory.
-
E.
Shannon entropy
Shannon entropy is a fundamental measure in information theory that quantifies the average uncertainty or information content in a random variable or message source.
- 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_69c699562484819086752091e3164a27 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c701c1383c8190ab5bf803bd6211a9 |
completed | March 27, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c89b260000819088d744ea8dc53cd2 |
completed | March 29, 2026, 3:23 a.m. |
Created at: March 27, 2026, 4 p.m.