Triple

T24346500
Position Surface form Disambiguated ID Type / Status
Subject Wisconsin Athletic Hall of Fame E613654 entity
Predicate notableInductee P7102 FINISHED
Object Pat Richter
Pat Richter is a former University of Wisconsin football star, NFL player, and influential athletic director who revitalized Wisconsin's athletic program.
E1646204 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: Pat Richter | Statement: [Wisconsin Athletic Hall of Fame, notableInductee, Pat Richter]
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: Pat Richter
Triple: [Wisconsin Athletic Hall of Fame, notableInductee, Pat Richter]
Generated description
Pat Richter is a former University of Wisconsin football star, NFL player, and influential athletic director who revitalized Wisconsin's athletic program.

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_69e2d7ddd29481909e7f539a6072bd71 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f2932978b88190afc441a3d4805e5f completed April 29, 2026, 11:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a100fd7da488190b404f382f8151f08 completed May 22, 2026, 8:12 a.m.
NEDg Description generation batch_6a10136871588190b4e4b4618ab7a400 completed May 22, 2026, 8:27 a.m.
NED2 Entity disambiguation (via description) batch_6a10141161b08190b471a7882a4d8aa0 completed May 22, 2026, 8:30 a.m.
Created at: April 18, 2026, 1:58 a.m.