Triple

T30085733
Position Surface form Disambiguated ID Type / Status
Subject Eugen Slutsky E764595 entity
Predicate knownFor P22 FINISHED
Object Slutsky theorem in probability theory
Slutsky theorem in probability theory is a fundamental result that describes how convergence in distribution and convergence in probability of random variables interact to determine the limiting behavior of their sums, products, and ratios.
E1900899 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: Slutsky theorem in probability theory | Statement: [Eugen Slutsky, knownFor, Slutsky theorem in probability theory]
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: Slutsky theorem in probability theory
Triple: [Eugen Slutsky, knownFor, Slutsky theorem in probability theory]
Generated description
Slutsky theorem in probability theory is a fundamental result that describes how convergence in distribution and convergence in probability of random variables interact to determine the limiting behavior of their sums, products, and ratios.

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_69f22473c0fc8190a926a8051b3b378b completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67d6c7874819094e666ddb8c1059f completed May 2, 2026, 10:40 p.m.
NED1 Entity disambiguation (via context triple) batch_6a274ca053388190a71d413043b8f764 completed June 8, 2026, 11:13 p.m.
NEDg Description generation batch_6a274da2b4f08190b54ffb23bd8b28dc completed June 8, 2026, 11:17 p.m.
NED2 Entity disambiguation (via description) batch_6a274e7037d48190869592da30780fc0 completed June 8, 2026, 11:21 p.m.
Created at: April 29, 2026, 7:04 p.m.