Triple

T21439358

Position	Surface form	Disambiguated ID	Type / Status
Subject	名谷キャンパス	`E528897`	entity
Predicate	所在区	`P83410`	FINISHED
Object	須磨区須磨区は、神戸市の西部に位置し、海岸や住宅地、大学キャンパスなどが広がる行政区です。	`E1484986`	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: 須磨区 | Statement: [名谷キャンパス, 所在区, 須磨区]

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

Target entity: 須磨区
Context triple: [名谷キャンパス, 所在区, 須磨区]

A. 播磨地方
播磨地方は、現在の兵庫県南西部に位置し、古代から交通と産業の要衝として発展してきた歴史ある地域です。
B. 大阪市港区
大阪市港区 is one of Osaka City's 24 wards, located along Osaka Bay and known for its port facilities, aquarium, and waterfront attractions.
C. 磯子区
磯子区 is one of the 18 administrative wards of Yokohama in Kanagawa Prefecture, Japan, known as a coastal industrial and residential area facing Tokyo Bay.
D. 木津川市
木津川市は、京都府南部に位置し、奈良県に隣接する住宅都市・歴史観光地として発展している市です。
E. 宍粟市
宍粟市は、兵庫県西部の中国山地に位置し、豊かな森林資源と自然環境を特徴とする市です。
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: 須磨区
Triple: [名谷キャンパス, 所在区, 須磨区]

Generated description

須磨区は、神戸市の西部に位置し、海岸や住宅地、大学キャンパスなどが広がる行政区です。

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

Target entity: 須磨区
Target entity description: 須磨区は、神戸市の西部に位置し、海岸や住宅地、大学キャンパスなどが広がる行政区です。

A. 播磨地方
播磨地方は、現在の兵庫県南西部に位置し、古代から交通と産業の要衝として発展してきた歴史ある地域です。
B. 大阪市港区
大阪市港区 is one of Osaka City's 24 wards, located along Osaka Bay and known for its port facilities, aquarium, and waterfront attractions.
C. 磯子区
磯子区 is one of the 18 administrative wards of Yokohama in Kanagawa Prefecture, Japan, known as a coastal industrial and residential area facing Tokyo Bay.
D. 木津川市
木津川市は、京都府南部に位置し、奈良県に隣接する住宅都市・歴史観光地として発展している市です。
E. 宍粟市
宍粟市は、兵庫県西部の中国山地に位置し、豊かな森林資源と自然環境を特徴とする市です。
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_69e0c4569fa081908101baa24f8745db`	completed	April 16, 2026, 11:13 a.m.
NER	Named-entity recognition	`batch_69e8b6feb2e48190ba5649f16a8bbbda`	completed	April 22, 2026, 11:54 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a09c8ff456481908092ae0af1297aab`	completed	May 17, 2026, 1:56 p.m.
NEDg	Description generation	`batch_6a09c99200a88190ad13565044dfcac0`	completed	May 17, 2026, 1:58 p.m.
NED2	Entity disambiguation (via description)	`batch_6a09ca47743081908cf7f9211eeedcc6`	completed	May 17, 2026, 2:01 p.m.

Created at: April 16, 2026, 6:04 p.m.