Triple

T2903185
Position Surface form Disambiguated ID Type / Status
Subject Slow Learner E62697 entity
Predicate containsWork P2011 FINISHED
Object Entropy
"Entropy" is a work—likely a song or track—by the artist Slow Learner, reflecting their style and thematic approach.
 E309710 NE FINISHED

How this triple was built (4 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: Entropy | Statement: [Slow Learner, containsWork, Entropy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Entropy
Context triple: [Slow Learner, containsWork, Entropy]
  • A. 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.
  • B. Rényi entropy
    Rényi entropy is a generalized measure of information and uncertainty that extends Shannon entropy by introducing a tunable order parameter to emphasize different aspects of a probability distribution.
  • C. information theory
    Information theory is a mathematical framework for quantifying information, communication, and data compression, foundational to modern digital communication and signal processing.
  • D. Shannon–Khinchin axioms
    The Shannon–Khinchin axioms are a set of fundamental conditions that uniquely characterize Shannon entropy as the standard measure of information and uncertainty in probability theory and information theory.
  • E. Tsallis entropy
    Tsallis entropy is a generalized, nonadditive entropy measure in statistical mechanics and information theory that extends Shannon entropy to better describe complex, nonextensive systems.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Entropy
Triple: [Slow Learner, containsWork, Entropy]
Generated description
"Entropy" is a work—likely a song or track—by the artist Slow Learner, reflecting their style and thematic approach.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Entropy
Target entity description: "Entropy" is a work—likely a song or track—by the artist Slow Learner, reflecting their style and thematic approach.
  • A. 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.
  • B. Rényi entropy
    Rényi entropy is a generalized measure of information and uncertainty that extends Shannon entropy by introducing a tunable order parameter to emphasize different aspects of a probability distribution.
  • C. information theory
    Information theory is a mathematical framework for quantifying information, communication, and data compression, foundational to modern digital communication and signal processing.
  • D. Shannon–Khinchin axioms
    The Shannon–Khinchin axioms are a set of fundamental conditions that uniquely characterize Shannon entropy as the standard measure of information and uncertainty in probability theory and information theory.
  • E. Tsallis entropy
    Tsallis entropy is a generalized, nonadditive entropy measure in statistical mechanics and information theory that extends Shannon entropy to better describe complex, nonextensive systems.
  • F. None of above. chosen

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_69ab4c3e070c8190b78d3d2c005876dd completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abe0b5546c8190852d9a454e41eee6 completed March 7, 2026, 8:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69b0560885688190b5bb51f40d555c4d completed March 10, 2026, 5:34 p.m.
NEDg Description generation batch_69b0638095b48190b3d7afa5eeef3e7a completed March 10, 2026, 6:31 p.m.
NED2 Entity disambiguation (via description) batch_69b063edf4ac8190bbcd430c8b584c95 completed March 10, 2026, 6:33 p.m.
Created at: March 6, 2026, 10:10 p.m.