Triple

T38678556
Position Surface form Disambiguated ID Type / Status
Subject 1997 Rose Bowl E943823 entity
Predicate mostValuablePlayer P2630 FINISHED
Object Joe Germaine
Joe Germaine is a former American football quarterback best known for leading Ohio State to a last-minute victory in the 1997 Rose Bowl before going on to play in the NFL and Arena Football League.
E2279542 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: Joe Germaine | Statement: [1997 Rose Bowl, mostValuablePlayer, Joe Germaine]
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: Joe Germaine
Triple: [1997 Rose Bowl, mostValuablePlayer, Joe Germaine]
Generated description
Joe Germaine is a former American football quarterback best known for leading Ohio State to a last-minute victory in the 1997 Rose Bowl before going on to play in the NFL and Arena Football League.

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_69f76eec28708190b9c82a505fc278e0 completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69fcdc3b93b88190a39ecec8f0a809ae completed May 7, 2026, 6:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41fd73532c8190adb1b4d0bb715362 completed June 29, 2026, 5:06 a.m.
NEDg Description generation batch_6a41fdf07718819099648fb5261a7458 completed June 29, 2026, 5:09 a.m.
NED2 Entity disambiguation (via description) batch_6a41fe64267481909cd252fcf484afaa completed June 29, 2026, 5:11 a.m.
Created at: May 3, 2026, 4:33 p.m.