Triple

T33805611
Position Surface form Disambiguated ID Type / Status
Subject Michael Frayn bibliography E866377 entity
Predicate includesNotableNovel P204258 FINISHED
Object The Trick of It
The Trick of It is a comic novel by Michael Frayn that explores the fraught relationship between a literary academic and the celebrated woman writer he studies.
 E2068276 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: The Trick of It | Statement: [Michael Frayn bibliography, includesNotableNovel, The Trick of It]
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: The Trick of It
Triple: [Michael Frayn bibliography, includesNotableNovel, The Trick of It]
Generated description
The Trick of It is a comic novel by Michael Frayn that explores the fraught relationship between a literary academic and the celebrated woman writer he studies.

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_69f3499057fc81909d862b1309a3bd71 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_6a035a4feb848190a6a297b46fd5c70f completed May 12, 2026, 4:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a36659b34c4819086e66a399e0fe25b completed June 20, 2026, 10:04 a.m.
NEDg Description generation batch_6a36665237cc8190bc8377bb72784058 completed June 20, 2026, 10:07 a.m.
NED2 Entity disambiguation (via description) batch_6a36676a438881909d01a02d62eccfdc completed June 20, 2026, 10:11 a.m.
Created at: May 1, 2026, 1:46 a.m.