Triple

T29175755
Position Surface form Disambiguated ID Type / Status
Subject 500 m speed skating at the 2006 Winter Olympics E739602 entity
Predicate goldMedalist P15190 FINISHED
Object Joey Cheek
Joey Cheek is an American speed skater and Olympic champion best known for winning gold at the 2006 Winter Games and donating his bonus money to humanitarian causes.
E1854150 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: Joey Cheek | Statement: [500 m speed skating at the 2006 Winter Olympics, goldMedalist, Joey Cheek]
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: Joey Cheek
Triple: [500 m speed skating at the 2006 Winter Olympics, goldMedalist, Joey Cheek]
Generated description
Joey Cheek is an American speed skater and Olympic champion best known for winning gold at the 2006 Winter Games and donating his bonus money to humanitarian causes.

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_69f07cb6394c8190ab7842c48e699e2a completed April 28, 2026, 9:24 a.m.
NER Named-entity recognition batch_69f663403c048190ae61c2a304e59d5a completed May 2, 2026, 8:49 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25507a18f88190b8017dbb1bc7b102 completed June 7, 2026, 11:05 a.m.
NEDg Description generation batch_6a25547cd54881909c2cdf767f15c71a completed June 7, 2026, 11:22 a.m.
NED2 Entity disambiguation (via description) batch_6a25602c1134819088fc1b0b5e3570e3 completed June 7, 2026, 12:12 p.m.
Created at: April 28, 2026, 11:54 a.m.