Triple

T30128232
Position Surface form Disambiguated ID Type / Status
Subject Frank Yates E765758 entity
Predicate knownFor P22 FINISHED
Object Yates’s correction for continuity
Yates’s correction for continuity is a statistical adjustment applied to chi-squared tests for 2×2 contingency tables to improve the approximation of discrete data by a continuous distribution, especially with small sample sizes.
 E1619452 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: Yates’s correction for continuity | Statement: [Frank Yates, knownFor, Yates’s correction for continuity]
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: Yates’s correction for continuity
Triple: [Frank Yates, knownFor, Yates’s correction for continuity]
Generated description
Yates’s correction for continuity is a statistical adjustment applied to chi-squared tests for 2×2 contingency tables to improve the approximation of discrete data by a continuous distribution, especially with small sample sizes.

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_69f22477d1a081908df2b7e6ed16859d completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67e4658088190962d0b05f9149b0b completed May 2, 2026, 10:44 p.m.
NED1 Entity disambiguation (via context triple) batch_6a274caebbf08190be433acb5ee75ef0 completed June 8, 2026, 11:13 p.m.
NEDg Description generation batch_6a274d73a2708190b25454c17b8991e2 completed June 8, 2026, 11:17 p.m.
NED2 Entity disambiguation (via description) batch_6a274e0019dc81908c8911898b2336a9 completed June 8, 2026, 11:19 p.m.
Created at: April 29, 2026, 7:14 p.m.