Triple

T22980223
Position Surface form Disambiguated ID Type / Status
Subject Could I Leave You? E571437 entity
Predicate sungByCharacter P14884 FINISHED
Object Phyllis Rogers Stone
Phyllis Rogers Stone is a central, emotionally complex character in Stephen Sondheim’s musical "Follies," known for her sharp wit and disillusionment with her marriage.
E1629149 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: Phyllis Rogers Stone | Statement: [Could I Leave You?, sungByCharacter, Phyllis Rogers Stone]
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: Phyllis Rogers Stone
Triple: [Could I Leave You?, sungByCharacter, Phyllis Rogers Stone]
Generated description
Phyllis Rogers Stone is a central, emotionally complex character in Stephen Sondheim’s musical "Follies," known for her sharp wit and disillusionment with her marriage.

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_69e245b3c50481908bb3741ec9f40862 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f18294c4c8819083ef85d9cb736613 completed April 29, 2026, 4:01 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fc985aba481908d88ba63e510b04c completed May 22, 2026, 3:12 a.m.
NEDg Description generation batch_6a0fcb9821dc81909eda37ccba173c7c completed May 22, 2026, 3:20 a.m.
NED2 Entity disambiguation (via description) batch_6a0fcc2cd0108190a7531d50f6be2386 completed May 22, 2026, 3:23 a.m.
Created at: April 17, 2026, 3:49 p.m.