Triple

T29133441
Position Surface form Disambiguated ID Type / Status
Subject Take a Girl Like You E738442 entity
Predicate mainCharacter P1183 FINISHED
Object Jenny Bunn
Jenny Bunn is the naive yet strong-willed young woman at the center of Kingsley Amis’s novel "Take a Girl Like You," whose moral convictions and romantic entanglements drive the story’s exploration of sex and social mores in 1960s England.
E1859898 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: Jenny Bunn | Statement: [Take a Girl Like You, mainCharacter, Jenny Bunn]
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: Jenny Bunn
Triple: [Take a Girl Like You, mainCharacter, Jenny Bunn]
Generated description
Jenny Bunn is the naive yet strong-willed young woman at the center of Kingsley Amis’s novel "Take a Girl Like You," whose moral convictions and romantic entanglements drive the story’s exploration of sex and social mores in 1960s England.

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_69f07cb3adb48190a9e0e169cd026634 completed April 28, 2026, 9:24 a.m.
NER Named-entity recognition batch_69f6622f519881909a265ebf46433064 completed May 2, 2026, 8:44 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25890c1b748190824ba7c8a4d68615 completed June 7, 2026, 3:06 p.m.
NEDg Description generation batch_6a25944a643c8190b5458a00aa75a9fc completed June 7, 2026, 3:54 p.m.
NED2 Entity disambiguation (via description) batch_6a25949da2f88190bab7ab9362f3c3d8 completed June 7, 2026, 3:56 p.m.
Created at: April 28, 2026, 11:33 a.m.