Triple

T26960536

Position	Surface form	Disambiguated ID	Type / Status
Subject	Amsterdam Metro rolling stock fleet	`E679027`	entity
Predicate	includesManufacturer	`P51141`	FINISHED
Object	CAF CAF is a Spanish multinational company that designs and manufactures railway vehicles and related transport equipment used in metro, commuter, and high-speed rail systems worldwide.	`E59649`	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: CAF | Statement: [Amsterdam Metro rolling stock fleet, includesManufacturer, CAF]

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: CAF
Triple: [Amsterdam Metro rolling stock fleet, includesManufacturer, CAF]

Generated description

CAF is a Spanish multinational company that designs and manufactures railway vehicles and related transport equipment used in metro, commuter, and high-speed rail systems worldwide.

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

Target predicate: includesManufacturer
Context triple: [Amsterdam Metro rolling stock fleet, includesManufacturer, CAF]

A. usedManufacturer
Indicates that an entity has employed or sourced products, components, or services from a particular manufacturer.
B. manufacturerType
Indicates the classification or category of a manufacturer based on its role, characteristics, or production type.
C. notableManufacturer chosen
Indicates that an entity is a well-known or prominent producer or maker of another entity.
D. manufacturerSupportedBy
Indicates that a manufacturer receives backing, assistance, or endorsement from another party.
E. alsoManufacturedBy
Indicates that an item or product is produced by an additional manufacturer beyond the primary or previously mentioned one.
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_69eeeb4f3a448190b1e94b2d4776c16e`	completed	April 27, 2026, 4:51 a.m.
NER	Named-entity recognition	`batch_69fd91a5dad8819093eeeef527027890`	completed	May 8, 2026, 7:32 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a12299ee29c81909d86c73a98faafb8`	completed	May 23, 2026, 10:26 p.m.
NEDg	Description generation	`batch_6a122a6dd674819088bf5cf55ac55ec5`	completed	May 23, 2026, 10:30 p.m.
NED2	Entity disambiguation (via description)	`batch_6a122b453e888190a195e8247f682604`	completed	May 23, 2026, 10:33 p.m.
PD	Predicate disambiguation	`batch_69fd8f65fe9081908902500a3228d935`	completed	May 8, 2026, 7:23 a.m.

Created at: April 27, 2026, 6:30 a.m.