Triple

T17125640

Position	Surface form	Disambiguated ID	Type / Status
Subject	Musashi-Kosugi Station	`E415585`	entity
Predicate	hasOperatorSection	`P126198`	FINISHED
Object	Tokyu station complex Tokyu station complex is a major railway and commercial hub operated by Tokyu Corporation, integrating multiple train lines with extensive shopping and urban facilities.	`E25657`	NE FINISHED

How this triple was built (2 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: Tokyu station complex | Statement: [Musashi-Kosugi Station, hasOperatorSection, Tokyu station complex]

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: Tokyu station complex
Triple: [Musashi-Kosugi Station, hasOperatorSection, Tokyu station complex]

Generated description

Tokyu station complex is a major railway and commercial hub operated by Tokyu Corporation, integrating multiple train lines with extensive shopping and urban facilities.

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_69d886d090cc8190a39cb94992586905`	completed	April 10, 2026, 5:12 a.m.
NER	Named-entity recognition	`batch_69e3f026ae188190b8c1e08529719878`	completed	April 18, 2026, 8:57 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a0f7e3417248190b6d2f9d80e26dbc1`	completed	May 21, 2026, 9:50 p.m.
NEDg	Description generation	`batch_6a0f7f4ce09081908de47029b8ffc097`	completed	May 21, 2026, 9:55 p.m.
NED2	Entity disambiguation (via description)	`batch_6a0f7fe7f7248190a377212661dd56b1`	completed	May 21, 2026, 9:58 p.m.

Created at: April 10, 2026, 5:36 a.m.