Triple

T22603101

Position	Surface form	Disambiguated ID	Type / Status
Subject	Shimo Senbon	`E574881`	entity
Predicate	hasJapaneseName	`P9882`	FINISHED
Object	下千本下千本（しもせんぼん）は、奈良県吉野山の桜名所エリアの一つで、山麓一帯に広がる桜の群生地として知られています。	`E1544505`	NE FINISHED

How this triple was built (4 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: 下千本 | Statement: [Shimo Senbon, hasJapaneseName, 下千本]

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07

Target entity: 下千本
Context triple: [Shimo Senbon, hasJapaneseName, 下千本]

A. 山下
山下 is a Japanese surname borne by various notable figures in politics, arts, sports, and the military.
B. 松下村塾
松下村塾は、幕末期の長州藩で吉田松陰が主宰し、多くの維新志士を輩出した私塾である。
C. 下野国
下野国 was an old Japanese province located in what is now Tochigi Prefecture, known historically as a key inland region of Honshū.
D. 井深大
井深大は、ソニー株式会社の共同創業者として知られる日本の実業家・技術者です。
E. 若尾文子
若尾文子は、1950年代から数多くの映画で活躍し、日本映画界を代表する名女優として知られる人物である。
F. None of above. chosen
G. Unsure - the case is ambiguous/there is not enough information to decide.

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: 下千本
Triple: [Shimo Senbon, hasJapaneseName, 下千本]

Generated description

下千本（しもせんぼん）は、奈良県吉野山の桜名所エリアの一つで、山麓一帯に広がる桜の群生地として知られています。

NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07

Target entity: 下千本
Target entity description: 下千本（しもせんぼん）は、奈良県吉野山の桜名所エリアの一つで、山麓一帯に広がる桜の群生地として知られています。

A. 山下
山下 is a Japanese surname borne by various notable figures in politics, arts, sports, and the military.
B. 松下村塾
松下村塾は、幕末期の長州藩で吉田松陰が主宰し、多くの維新志士を輩出した私塾である。
C. 下野国
下野国 was an old Japanese province located in what is now Tochigi Prefecture, known historically as a key inland region of Honshū.
D. 井深大
井深大は、ソニー株式会社の共同創業者として知られる日本の実業家・技術者です。
E. 若尾文子
若尾文子は、1950年代から数多くの映画で活躍し、日本映画界を代表する名女優として知られる人物である。
F. None of above. chosen

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_69e245bc11308190b69d794d5d1e0bb6`	completed	April 17, 2026, 2:37 p.m.
NER	Named-entity recognition	`batch_69f1626eb178819096866d03a78f82fc`	completed	April 29, 2026, 1:44 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a0b3d682f5081909814d3fd294221d9`	completed	May 18, 2026, 4:25 p.m.
NEDg	Description generation	`batch_6a0b3e9fac2c819085c7b7f2ee0abe17`	completed	May 18, 2026, 4:30 p.m.
NED2	Entity disambiguation (via description)	`batch_6a0b3f7afd2c81908c40d6479a826c22`	completed	May 18, 2026, 4:34 p.m.

Created at: April 17, 2026, 2:51 p.m.