Triple

T33668283
Position Surface form Disambiguated ID Type / Status
Subject Vermont Catamounts men's ice hockey E862548 entity
Predicate notableAlumnus P304 FINISHED
Object Aaron Miller
Aaron Miller is a former American professional ice hockey defenseman who played in the NHL and represented the United States internationally, including at the Olympics.
E2062721 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: Aaron Miller | Statement: [Vermont Catamounts men's ice hockey, notableAlumnus, Aaron Miller]
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: Aaron Miller
Triple: [Vermont Catamounts men's ice hockey, notableAlumnus, Aaron Miller]
Generated description
Aaron Miller is a former American professional ice hockey defenseman who played in the NHL and represented the United States internationally, including at the Olympics.

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_69f34984c4008190bb82f33a7819da64 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6fa3a06c48190b69d72ab4e82e852 completed May 3, 2026, 7:33 a.m.
NED1 Entity disambiguation (via context triple) batch_6a363c8e29b08190852a4102d3d9528e completed June 20, 2026, 7:09 a.m.
NEDg Description generation batch_6a36464372148190ab74a6c6f77dff2b completed June 20, 2026, 7:50 a.m.
NED2 Entity disambiguation (via description) batch_6a36478c793c8190a48758a42ac2337d completed June 20, 2026, 7:55 a.m.
Created at: May 1, 2026, 1:42 a.m.