Triple

T35369032
Position Surface form Disambiguated ID Type / Status
Subject Rick Vaive E1021716 entity
Predicate fullName P16 FINISHED
Object Richard Claude Vaive
Richard Claude Vaive is a former Canadian professional ice hockey right winger best known for being the first player in Toronto Maple Leafs history to score 50 goals in a season.
E2136172 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: Richard Claude Vaive | Statement: [Rick Vaive, fullName, Richard Claude Vaive]
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: Richard Claude Vaive
Triple: [Rick Vaive, fullName, Richard Claude Vaive]
Generated description
Richard Claude Vaive is a former Canadian professional ice hockey right winger best known for being the first player in Toronto Maple Leafs history to score 50 goals in a season.

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_69f76df000488190ab7c97f565677055 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f791d5286481908a4ddff27d75b3df completed May 3, 2026, 6:20 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3823e1430c8190a6c902cf947b09d0 completed June 21, 2026, 5:48 p.m.
NEDg Description generation batch_6a3824533b3c819082f13f9a231b9643 completed June 21, 2026, 5:50 p.m.
NED2 Entity disambiguation (via description) batch_6a3825338a88819090c8dfafb2dc5e42 completed June 21, 2026, 5:53 p.m.
Created at: May 3, 2026, 4:03 p.m.