Triple

T30344064

Position	Surface form	Disambiguated ID	Type / Status
Subject	Her Blue Sky	`E771826`	entity
Predicate	artDirector	`P7743`	FINISHED
Object	Yōko Kuno Yōko Kuno is a Japanese art director and animator known for her distinctive background and visual design work in anime films and series.	`E2170158`	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: Yōko Kuno | Statement: [Her Blue Sky, artDirector, Yōko Kuno]

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: Yōko Kuno
Triple: [Her Blue Sky, artDirector, Yōko Kuno]

Generated description

Yōko Kuno is a Japanese art director and animator known for her distinctive background and visual design work in anime films and series.

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_69f682065a548190bff089d7dafbf3ad`	completed	May 2, 2026, 11 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a38dde27c248190bcab513f78267892`	completed	June 22, 2026, 7:01 a.m.
NEDg	Description generation	`batch_6a38f12eeefc8190a92bf1d3004269ac`	completed	June 22, 2026, 8:24 a.m.
NED2	Entity disambiguation (via description)	`batch_6a38f35df5308190991210dda64a0083`	completed	June 22, 2026, 8:33 a.m.

Created at: April 29, 2026, 7:55 p.m.