Triple

T22533350

Position	Surface form	Disambiguated ID	Type / Status
Subject	東京都港区	`E557098`	entity
Predicate	containsDistrict	`P22582`	FINISHED
Object	芝浦芝浦は東京都港区の東京湾沿いに位置し、再開発が進むオフィスビルや高層マンション、運河沿いの景観で知られるウォーターフロントエリアである。	`E1541849`	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: 芝浦 | Statement: [東京都港区, containsDistrict, 芝浦]

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

Target entity: 芝浦
Context triple: [東京都港区, containsDistrict, 芝浦]

A. 田口
田口 is a common Japanese surname and given name used by various individuals across fields such as sports, entertainment, and academia.
B. 石川
石川は大阪府東部を流れ、大和川水系に属する中小河川です。
C. 二子玉川
二子玉川 is a riverside commercial and residential district in Tokyo known for its large shopping complexes, stylish cafes, and family-friendly urban development along the Tama River.
D. 吉井川
吉井川は、岡山県などを流れて瀬戸内海に注ぐ、中国地方を代表する一級河川の一つです。
E. 根津
根津は東京都文京区に位置し、古い町並みと根津神社で知られる歴史ある下町エリアです。
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: 芝浦
Triple: [東京都港区, containsDistrict, 芝浦]

Generated description

芝浦は東京都港区の東京湾沿いに位置し、再開発が進むオフィスビルや高層マンション、運河沿いの景観で知られるウォーターフロントエリアである。

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

Target entity: 芝浦
Target entity description: 芝浦は東京都港区の東京湾沿いに位置し、再開発が進むオフィスビルや高層マンション、運河沿いの景観で知られるウォーターフロントエリアである。

A. 田口
田口 is a common Japanese surname and given name used by various individuals across fields such as sports, entertainment, and academia.
B. 石川
石川は大阪府東部を流れ、大和川水系に属する中小河川です。
C. 二子玉川
二子玉川 is a riverside commercial and residential district in Tokyo known for its large shopping complexes, stylish cafes, and family-friendly urban development along the Tama River.
D. 吉井川
吉井川は、岡山県などを流れて瀬戸内海に注ぐ、中国地方を代表する一級河川の一つです。
E. 根津
根津は東京都文京区に位置し、古い町並みと根津神社で知られる歴史ある下町エリアです。
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_69e11e57483c8190b0887c4f8ff26446`	completed	April 16, 2026, 5:37 p.m.
NER	Named-entity recognition	`batch_69f15ed88cf08190ae7e5b6bf9a80372`	completed	April 29, 2026, 1:28 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a0b1ddee4f48190972567034fa0c9d7`	completed	May 18, 2026, 2:10 p.m.
NEDg	Description generation	`batch_6a0b1f09934c81909c0d81a7838f5580`	completed	May 18, 2026, 2:15 p.m.
NED2	Entity disambiguation (via description)	`batch_6a0b258107088190ba81cb2d0a34209b`	completed	May 18, 2026, 2:43 p.m.

Created at: April 16, 2026, 8:51 p.m.