Triple

T26627212

Position	Surface form	Disambiguated ID	Type / Status
Subject	Jerzy Neyman	`E668387`	entity
Predicate	notableWork	`P4`	FINISHED
Object	Neyman–Scott problem The Neyman–Scott problem is a classic example in statistical inference that illustrates how maximum likelihood estimation can fail to produce consistent estimators in the presence of many nuisance parameters.	`E1735037`	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: Neyman–Scott problem | Statement: [Jerzy Neyman, notableWork, Neyman–Scott problem]

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: Neyman–Scott problem
Triple: [Jerzy Neyman, notableWork, Neyman–Scott problem]

Generated description

The Neyman–Scott problem is a classic example in statistical inference that illustrates how maximum likelihood estimation can fail to produce consistent estimators in the presence of many nuisance parameters.

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_69ee9cff507c819092b95bf7219a702e`	completed	April 26, 2026, 11:17 p.m.
NER	Named-entity recognition	`batch_69f615ea59048190880a13cb9a9f8126`	completed	May 2, 2026, 3:19 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a11ec343ae48190860511c4d420194f`	completed	May 23, 2026, 6:04 p.m.
NEDg	Description generation	`batch_6a11eda28c48819098126d5c8d74adff`	completed	May 23, 2026, 6:10 p.m.
NED2	Entity disambiguation (via description)	`batch_6a11ee308af88190b08944270a2fd1d8`	completed	May 23, 2026, 6:13 p.m.

Created at: April 27, 2026, 2:23 a.m.