Triple

T32592008
Position Surface form Disambiguated ID Type / Status
Subject Will Grayson, Will Grayson E833094 entity
Predicate titleCharacter P9202 FINISHED
Object Will Grayson
Will Grayson is one of the two teenage protagonists whose intersecting lives drive the plot of the young adult novel "Will Grayson, Will Grayson" by John Green and David Levithan.
E2012289 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: Will Grayson | Statement: [Will Grayson, Will Grayson, titleCharacter, Will Grayson]
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: Will Grayson
Triple: [Will Grayson, Will Grayson, titleCharacter, Will Grayson]
Generated description
Will Grayson is one of the two teenage protagonists whose intersecting lives drive the plot of the young adult novel "Will Grayson, Will Grayson" by John Green and David Levithan.

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_69f34929ff648190aded9424aa7564ae completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c691ce288190abcc586dd488079c completed May 3, 2026, 3:52 a.m.
NED1 Entity disambiguation (via context triple) batch_6a347bac58f48190958c4bc20ac180ed completed June 18, 2026, 11:13 p.m.
NEDg Description generation batch_6a347c29d29c819085157ac0ed98a2f0 completed June 18, 2026, 11:15 p.m.
NED2 Entity disambiguation (via description) batch_6a347d86b008819099f202b5d0f6a0d6 completed June 18, 2026, 11:21 p.m.
Created at: May 1, 2026, 1:05 a.m.