Triple

T29453507
Position Surface form Disambiguated ID Type / Status
Subject Alerus Financial E747038 entity
Predicate hasDivision P35 FINISHED
Object retirement and benefits division
The retirement and benefits division is a specialized unit of Alerus Financial that provides retirement planning and employee benefits services to individuals and organizations.
E1868072 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: retirement and benefits division | Statement: [Alerus Financial, hasDivision, retirement and benefits division]
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: retirement and benefits division
Triple: [Alerus Financial, hasDivision, retirement and benefits division]
Generated description
The retirement and benefits division is a specialized unit of Alerus Financial that provides retirement planning and employee benefits services to individuals and organizations.

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_69f0a7a230488190b44a97fe3d16f731 completed April 28, 2026, 12:27 p.m.
NER Named-entity recognition batch_69f66b69acc08190b571a81fb53cf718 completed May 2, 2026, 9:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25d941cc9c8190b46bcc0be931bdf7 completed June 7, 2026, 8:49 p.m.
NEDg Description generation batch_6a25dd6da0f4819097a6c39da69d5e6e completed June 7, 2026, 9:06 p.m.
NED2 Entity disambiguation (via description) batch_6a25e1a372c8819098b3fe3c7152633b completed June 7, 2026, 9:24 p.m.
Created at: April 28, 2026, 3:34 p.m.