Triple

T35924029
Position Surface form Disambiguated ID Type / Status
Subject John Darnielle E1038969 entity
Predicate notableWork P4 FINISHED
Object Wolf in White Van
Wolf in White Van is a critically acclaimed debut novel by musician and author John Darnielle that explores trauma, isolation, and fantasy through the story of a reclusive creator of a mail-based role-playing game.
 E2161828 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: Wolf in White Van | Statement: [John Darnielle, notableWork, Wolf in White Van]
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: Wolf in White Van
Triple: [John Darnielle, notableWork, Wolf in White Van]
Generated description
Wolf in White Van is a critically acclaimed debut novel by musician and author John Darnielle that explores trauma, isolation, and fantasy through the story of a reclusive creator of a mail-based role-playing game.

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_69f76e2320748190b7f5c4750d0cd0d3 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7ab7ac6188190b571ed841b9f90f2 completed May 3, 2026, 8:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38ae381da08190907eba4ca2bd0cbe completed June 22, 2026, 3:38 a.m.
NEDg Description generation batch_6a38aed9c3608190be4d8a738722fc4f completed June 22, 2026, 3:41 a.m.
NED2 Entity disambiguation (via description) batch_6a38afe3882c819094065a02436b8868 completed June 22, 2026, 3:45 a.m.
Created at: May 3, 2026, 4:07 p.m.