Triple

T32747346
Position Surface form Disambiguated ID Type / Status
Subject Ernst Hellinger E837389 entity
Predicate notableConcept P201 FINISHED
Object Hellinger–Toeplitz theorem
The Hellinger–Toeplitz theorem is a fundamental result in functional analysis stating that any everywhere-defined symmetric linear operator on a Hilbert space must be bounded.
 E2020511 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: Hellinger–Toeplitz theorem | Statement: [Ernst Hellinger, notableConcept, Hellinger–Toeplitz 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: Hellinger–Toeplitz theorem
Triple: [Ernst Hellinger, notableConcept, Hellinger–Toeplitz theorem]
Generated description
The Hellinger–Toeplitz theorem is a fundamental result in functional analysis stating that any everywhere-defined symmetric linear operator on a Hilbert space must be bounded.

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_69f34936e1748190b797e406e4e9293a completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6cc1fcf0881908c53879f48893952 completed May 3, 2026, 4:16 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34a7b5e5ec8190b15583ec64c2390a completed June 19, 2026, 2:21 a.m.
NEDg Description generation batch_6a34a84e9e3881909614d79de44dd3cc completed June 19, 2026, 2:24 a.m.
NED2 Entity disambiguation (via description) batch_6a34a8e08dc8819095892aefb102d650 completed June 19, 2026, 2:26 a.m.
Created at: May 1, 2026, 1:12 a.m.