Triple

T29106968

Position	Surface form	Disambiguated ID	Type / Status
Subject	Kansai Ki-in	`E736786`	entity
Predicate	hasDivision	`P35`	FINISHED
Object	insei training system The insei training system is a structured program for young Go prodigies to rigorously study and compete with the goal of becoming professional players.	`E1849471`	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: insei training system | Statement: [Kansai Ki-in, hasDivision, insei training system]

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: insei training system
Triple: [Kansai Ki-in, hasDivision, insei training system]

Generated description

The insei training system is a structured program for young Go prodigies to rigorously study and compete with the goal of becoming professional players.

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_69f077ec765c81909474c88bcc8bab43`	completed	April 28, 2026, 9:03 a.m.
NER	Named-entity recognition	`batch_69f661ba062881909fa3d7b23938e2ab`	completed	May 2, 2026, 8:42 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a2537c079688190b8142efa06575188`	completed	June 7, 2026, 9:20 a.m.
NEDg	Description generation	`batch_6a253bde7d1c819082d2aeac0b835460`	completed	June 7, 2026, 9:37 a.m.
NED2	Entity disambiguation (via description)	`batch_6a253fc091b8819091f9253f88e27df4`	completed	June 7, 2026, 9:54 a.m.

Created at: April 28, 2026, 11:16 a.m.