Triple

T8354658

Position	Surface form	Disambiguated ID	Type / Status
Subject	中部地方	`E196653`	entity
Predicate	hasRiver	`P165`	FINISHED
Object	庄川庄川は日本の中部地方を流れ、富山県の庄川峡などの景勝地や水力発電で知られる河川である。	`E727576`	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: 庄川 | Statement: [中部地方, hasRiver, 庄川]

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

Target entity: 庄川
Context triple: [中部地方, hasRiver, 庄川]

A. 桂川
桂川は、京都市内を流れ嵐山の景観で知られる日本の代表的な河川の一つです。
B. 池田
池田 is a common Japanese surname and place name associated with various regions, historical figures, and businesses in Japan.
C. 三川軍一
三川軍一 was an Imperial Japanese Navy admiral best known for commanding Japanese forces to victory at the 1942 Battle of Savo Island during World War II.
D. 西村祥治
西村祥治 was an Imperial Japanese Navy admiral during World War II who commanded major surface forces in the Pacific War.
E. 佐藤
佐藤 is a very common Japanese surname, often romanized as Satō or Sato.
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: 庄川
Triple: [中部地方, hasRiver, 庄川]

Generated description

庄川は日本の中部地方を流れ、富山県の庄川峡などの景勝地や水力発電で知られる河川である。

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

Target entity: 庄川
Target entity description: 庄川は日本の中部地方を流れ、富山県の庄川峡などの景勝地や水力発電で知られる河川である。

A. 桂川
桂川は、京都市内を流れ嵐山の景観で知られる日本の代表的な河川の一つです。
B. 池田
池田 is a common Japanese surname and place name associated with various regions, historical figures, and businesses in Japan.
C. 三川軍一
三川軍一 was an Imperial Japanese Navy admiral best known for commanding Japanese forces to victory at the 1942 Battle of Savo Island during World War II.
D. 西村祥治
西村祥治 was an Imperial Japanese Navy admiral during World War II who commanded major surface forces in the Pacific War.
E. 佐藤
佐藤 is a very common Japanese surname, often romanized as Satō or Sato.
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_69ca82f08b348190bfb7881944bbff6f`	completed	March 30, 2026, 2:04 p.m.
NER	Named-entity recognition	`batch_69cb8048edb88190a1980ad74818b898`	completed	March 31, 2026, 8:05 a.m.
NED1	Entity disambiguation (via context triple)	`batch_69cdc75e94288190ba1905dd4ca172dd`	completed	April 2, 2026, 1:33 a.m.
NEDg	Description generation	`batch_69cdcc86626c8190a4206feedea24b41`	completed	April 2, 2026, 1:55 a.m.
NED2	Entity disambiguation (via description)	`batch_69cdcde02e088190be8220f7d18d6700`	completed	April 2, 2026, 2:01 a.m.

Created at: March 30, 2026, 5:59 p.m.