Triple

T36005247

Position	Surface form	Disambiguated ID	Type / Status
Subject	R143 series	`E1041246`	entity
Predicate	builtAt	`P283`	FINISHED
Object	Kawasaki plant in Kobe, Japan The Kawasaki plant in Kobe, Japan is a major industrial manufacturing facility of Kawasaki Heavy Industries, known for producing railway vehicles and other heavy machinery.	`E2166235`	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: Kawasaki plant in Kobe, Japan | Statement: [R143 series, builtAt, Kawasaki plant in Kobe, Japan]

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: Kawasaki plant in Kobe, Japan
Triple: [R143 series, builtAt, Kawasaki plant in Kobe, Japan]

Generated description

The Kawasaki plant in Kobe, Japan is a major industrial manufacturing facility of Kawasaki Heavy Industries, known for producing railway vehicles and other heavy machinery.

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_69f76e2a02208190aedd1f9025a8b300`	completed	May 3, 2026, 3:47 p.m.
NER	Named-entity recognition	`batch_69f7acaec1508190a38f2ac9cc5383e7`	completed	May 3, 2026, 8:14 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a38cb8965fc8190a369486aa71e9256`	completed	June 22, 2026, 5:43 a.m.
NEDg	Description generation	`batch_6a38cc5342208190b0c94ec76b76b752`	completed	June 22, 2026, 5:46 a.m.
NED2	Entity disambiguation (via description)	`batch_6a38ccfbece08190be296926289702b2`	completed	June 22, 2026, 5:49 a.m.

Created at: May 3, 2026, 4:07 p.m.