Triple

T27605078
Position Surface form Disambiguated ID Type / Status
Subject Martin-Löf randomness E700156 entity
Predicate relatedTo P37 FINISHED
Object Levin–Schnorr theorem
The Levin–Schnorr theorem is a fundamental result in algorithmic randomness that characterizes Martin-Löf random sequences in terms of their incompressibility as measured by Kolmogorov complexity.
 E1780405 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: Levin–Schnorr theorem | Statement: [Martin-Löf randomness, relatedTo, Levin–Schnorr theorem]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Levin–Schnorr theorem
Triple: [Martin-Löf randomness, relatedTo, Levin–Schnorr theorem]
Generated description
The Levin–Schnorr theorem is a fundamental result in algorithmic randomness that characterizes Martin-Löf random sequences in terms of their incompressibility as measured by Kolmogorov complexity.

Provenance (5 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_69ef6a4e2e208190b63b7268f405785c completed April 27, 2026, 1:53 p.m.
NER Named-entity recognition batch_69f6309aa494819092c2b02bd6adaeb6 completed May 2, 2026, 5:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12d0e7cea08190bdca6497f79d2b5e completed May 24, 2026, 10:20 a.m.
NEDg Description generation batch_6a12d1497cb4819085e9a1a5401d9118 completed May 24, 2026, 10:22 a.m.
NED2 Entity disambiguation (via description) batch_6a12d2747f6881909aa2a5b0c389a494 completed May 24, 2026, 10:27 a.m.
Created at: April 27, 2026, 2:09 p.m.