Triple

T32152626
Position Surface form Disambiguated ID Type / Status
Subject Michigan v. Bryant E821198 entity
Predicate respondent P2238 FINISHED
Object Richard Bryant
Richard Bryant was the criminal defendant whose confrontation rights were at issue in the U.S. Supreme Court case Michigan v. Bryant, which addressed the admissibility of a dying victim’s statements to police.
E1996440 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 Bryant | Statement: [Michigan v. Bryant, respondent, Richard Bryant]
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 Bryant
Triple: [Michigan v. Bryant, respondent, Richard Bryant]
Generated description
Richard Bryant was the criminal defendant whose confrontation rights were at issue in the U.S. Supreme Court case Michigan v. Bryant, which addressed the admissibility of a dying victim’s statements to police.

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_69f3490520d081909b2f1271dab75faa completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6b9eccab88190a2895cbb0332c5a7 completed May 3, 2026, 2:58 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f0bcc17108190819daced37bcf89e completed June 14, 2026, 8:15 p.m.
NEDg Description generation batch_6a2f0c2f2dc08190908ce4c55cc626d7 completed June 14, 2026, 8:16 p.m.
NED2 Entity disambiguation (via description) batch_6a2f2fb4aed081909ce03b2c0ce4fc61 completed June 14, 2026, 10:48 p.m.
Created at: May 1, 2026, 12:31 a.m.