Triple

T11934277

Position	Surface form	Disambiguated ID	Type / Status
Subject	文京区	`E283998`	entity
Predicate	containsDistrict	`P22582`	FINISHED
Object	大塚大塚は、東京都文京区に位置する住宅地と商業地が混在した地域です。	`E954803`	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: 大塚 | Statement: [文京区, containsDistrict, 大塚]

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

Target entity: 大塚
Context triple: [文京区, containsDistrict, 大塚]

A. 半蔵門
半蔵門は、東京都千代田区に位置する皇居の西側に設けられた門で、江戸城時代からの歴史を持つ重要な出入口です。
B. 高輪
高輪 is a district in Minato, Tokyo, known for its mix of historic temples, residential areas, and proximity to major transport hubs like Shinagawa.
C. 伏見櫓
伏見櫓は、江戸城跡である東京の皇居内に現存する歴史的な三重櫓で、重要文化財にも指定されている城郭建築物である。
D. 赤坂御用地
赤坂御用地は、東京都港区赤坂に位置する日本の皇室関連施設が集まる広大な御用地で、皇族方の御所や宮邸が置かれているエリアである。
E. 稲荷山
稲荷山 is a sacred mountain in Kyoto, Japan, best known as the site of Fushimi Inari Taisha and its thousands of vermilion torii gates.
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: 大塚
Triple: [文京区, containsDistrict, 大塚]

Generated description

大塚は、東京都文京区に位置する住宅地と商業地が混在した地域です。

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

Target entity: 大塚
Target entity description: 大塚は、東京都文京区に位置する住宅地と商業地が混在した地域です。

A. 半蔵門
半蔵門は、東京都千代田区に位置する皇居の西側に設けられた門で、江戸城時代からの歴史を持つ重要な出入口です。
B. 高輪
高輪 is a district in Minato, Tokyo, known for its mix of historic temples, residential areas, and proximity to major transport hubs like Shinagawa.
C. 伏見櫓
伏見櫓は、江戸城跡である東京の皇居内に現存する歴史的な三重櫓で、重要文化財にも指定されている城郭建築物である。
D. 赤坂御用地
赤坂御用地は、東京都港区赤坂に位置する日本の皇室関連施設が集まる広大な御用地で、皇族方の御所や宮邸が置かれているエリアである。
E. 稲荷山
稲荷山 is a sacred mountain in Kyoto, Japan, best known as the site of Fushimi Inari Taisha and its thousands of vermilion torii gates.
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_69d6ab2ce9c48190b5d39511b524f666`	completed	April 8, 2026, 7:23 p.m.
NER	Named-entity recognition	`batch_69d90306fcf48190a963d2d1932288d1`	completed	April 10, 2026, 2:02 p.m.
NED1	Entity disambiguation (via context triple)	`batch_69f4407cc2388190b0f849fbeed89ab7`	completed	May 1, 2026, 5:56 a.m.
NEDg	Description generation	`batch_69f448fc874081908fe05f9d8aff11a3`	completed	May 1, 2026, 6:32 a.m.
NED2	Entity disambiguation (via description)	`batch_69f44afdc7b08190bdf47cfcb94c34c8`	completed	May 1, 2026, 6:41 a.m.

Created at: April 8, 2026, 9:45 p.m.