Triple

T30482060
Position Surface form Disambiguated ID Type / Status
Subject Nisio Isin E775612 entity
Predicate notableWork P4 FINISHED
Object Medaka Box
Medaka Box is a Japanese manga series written by Nisio Isin that follows the overachieving student council president Medaka Kurokami as she tackles increasingly bizarre and superpowered challenges at her high school.
E1917687 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: Medaka Box | Statement: [Nisio Isin, notableWork, Medaka Box]
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: Medaka Box
Triple: [Nisio Isin, notableWork, Medaka Box]
Generated description
Medaka Box is a Japanese manga series written by Nisio Isin that follows the overachieving student council president Medaka Kurokami as she tackles increasingly bizarre and superpowered challenges at her high school.

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_69f22497f91c8190afa7165bc900accd completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f68742cc6481908be525603fb6ba97 completed May 2, 2026, 11:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27ac26632081908a818730188a163c completed June 9, 2026, 6:01 a.m.
NEDg Description generation batch_6a27ad42b24481909895f7722fd747a8 completed June 9, 2026, 6:05 a.m.
NED2 Entity disambiguation (via description) batch_6a27ae21f75481909c18ec26978a3e79 completed June 9, 2026, 6:09 a.m.
Created at: April 29, 2026, 8:12 p.m.