Triple

T35169350
Position Surface form Disambiguated ID Type / Status
Subject Robbins–Monro algorithm E1015498 entity
Predicate relatedTo P37 FINISHED
Object Robbins–Siegmund theorem
The Robbins–Siegmund theorem is a fundamental result in probability theory that provides almost sure convergence criteria for certain classes of stochastic processes and martingales, widely used in analyzing stochastic approximation algorithms.
 E2127532 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: Robbins–Siegmund theorem | Statement: [Robbins–Monro algorithm, relatedTo, Robbins–Siegmund 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: Robbins–Siegmund theorem
Triple: [Robbins–Monro algorithm, relatedTo, Robbins–Siegmund theorem]
Generated description
The Robbins–Siegmund theorem is a fundamental result in probability theory that provides almost sure convergence criteria for certain classes of stochastic processes and martingales, widely used in analyzing stochastic approximation algorithms.

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_69f76ddbfde081908bffc91572368289 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f78d384610819098d02a111ca58a7c completed May 3, 2026, 6 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37d970dbc08190a5259b3f82c2c31a completed June 21, 2026, 12:30 p.m.
NEDg Description generation batch_6a37daa762448190b4169e58639fcdd4 completed June 21, 2026, 12:35 p.m.
NED2 Entity disambiguation (via description) batch_6a37dc0c0a5081908ce1002b7181b433 completed June 21, 2026, 12:41 p.m.
Created at: May 3, 2026, 4:02 p.m.