Triple

T30344303
Position Surface form Disambiguated ID Type / Status
Subject Monthly Shōnen Gangan E771832 entity
Predicate notableForSerializing P207230 FINISHED
Object The Case Study of Vanitas
The Case Study of Vanitas is a dark fantasy manga series by Jun Mochizuki that follows a human doctor using a mysterious grimoire to cure cursed vampires in an alternate steampunk Paris.
E1911638 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 Case Study of Vanitas | Statement: [Monthly Shōnen Gangan, notableForSerializing, The Case Study of Vanitas]
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 Case Study of Vanitas
Triple: [Monthly Shōnen Gangan, notableForSerializing, The Case Study of Vanitas]
Generated description
The Case Study of Vanitas is a dark fantasy manga series by Jun Mochizuki that follows a human doctor using a mysterious grimoire to cure cursed vampires in an alternate steampunk Paris.

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_69f2248b9a208190bc3e6804acd5afd6 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_6a03809725bc81909c8b61d72d72ca2b completed May 12, 2026, 7:33 p.m.
NED1 Entity disambiguation (via context triple) batch_6a277c2737348190b3dc8979f853cd3a completed June 9, 2026, 2:36 a.m.
NEDg Description generation batch_6a277eea31948190aa32de01ebaf1839 completed June 9, 2026, 2:48 a.m.
NED2 Entity disambiguation (via description) batch_6a27829f129c8190bb94438be86c5418 completed June 9, 2026, 3:03 a.m.
Created at: April 29, 2026, 7:55 p.m.