Triple

T26777813
Position Surface form Disambiguated ID Type / Status
Subject Hockey East Coach of the Year E670164 entity
Predicate hasRecipient P108 FINISHED
Object Kevin Sneddon
Kevin Sneddon is an American ice hockey coach best known for his long tenure leading the University of Vermont men’s hockey program in the NCAA’s Hockey East conference.
E1797129 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: Kevin Sneddon | Statement: [Hockey East Coach of the Year, hasRecipient, Kevin Sneddon]
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: Kevin Sneddon
Triple: [Hockey East Coach of the Year, hasRecipient, Kevin Sneddon]
Generated description
Kevin Sneddon is an American ice hockey coach best known for his long tenure leading the University of Vermont men’s hockey program in the NCAA’s Hockey East conference.

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_69eeb31c925881909b597f6e40056d28 completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f619778a588190a1d2375eff7a628f completed May 2, 2026, 3:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a131126061c819081c1c92f9ef833b8 completed May 24, 2026, 2:54 p.m.
NEDg Description generation batch_6a13123f14008190a62775eea01bd2d4 completed May 24, 2026, 2:59 p.m.
NED2 Entity disambiguation (via description) batch_6a1313d9f1688190ab230c0c39167e27 completed May 24, 2026, 3:06 p.m.
Created at: April 27, 2026, 4:06 a.m.