Triple

T24989824

Position	Surface form	Disambiguated ID	Type / Status
Subject	T-10	`E625414`	entity
Predicate	associatedStationJapaneseName	`P9882`	FINISHED
Object	日本橋駅日本橋駅は、東京都中央区の日本橋エリアに位置し、複数の地下鉄路線が乗り入れる主要なターミナル駅です。	`E1658031`	NE FINISHED

How this triple was built (3 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: 日本橋駅 | Statement: [T-10, associatedStationJapaneseName, 日本橋駅]

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: 日本橋駅
Triple: [T-10, associatedStationJapaneseName, 日本橋駅]

Generated description

日本橋駅は、東京都中央区の日本橋エリアに位置し、複数の地下鉄路線が乗り入れる主要なターミナル駅です。

PD Predicate disambiguation gpt-5-mini-2025-08-07

Target predicate: associatedStationJapaneseName
Context triple: [T-10, associatedStationJapaneseName, 日本橋駅]

A. adjacentStationOnTokaidoShinkansen
Indicates that one station is directly next to another along the Tokaido Shinkansen line, with no other station in between.
B. adjacentStationOnTokaidoMainLine
Indicates that one station is directly next to another station along the Tokaido Main Line, with no other stations in between.
C. associatedStation
Indicates a relationship where one entity is linked or connected to a particular station as its relevant or related station.
D. hasOfficialNameInJapanese chosen
Indicates that an entity has an official, formally recognized name expressed in the Japanese language.
E. adjacentStationOnNambuLine
Indicates that one station is directly next to another station along the Nambu railway line, with no other stations in between.
F. None of above.

Provenance (6 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_69e2ff2611c081908710457fbe6d376b`	completed	April 18, 2026, 3:48 a.m.
NER	Named-entity recognition	`batch_69f6db1f3ec48190a82e7d893d3c76ba`	completed	May 3, 2026, 5:20 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a103367eca08190a5cb236020e2dd91`	completed	May 22, 2026, 10:43 a.m.
NEDg	Description generation	`batch_6a103440175081908c16266d18fa3f7f`	completed	May 22, 2026, 10:47 a.m.
NED2	Entity disambiguation (via description)	`batch_6a1035004ea081908dc1f871f02ad95b`	completed	May 22, 2026, 10:50 a.m.
PD	Predicate disambiguation	`batch_69f6d82adfa481908a5e196d2e18c73f`	completed	May 3, 2026, 5:07 a.m.

Created at: April 18, 2026, 6:03 a.m.