Triple

T34400327

Position	Surface form	Disambiguated ID	Type / Status
Subject	SS Empress of Canada (1964)	`E882959`	entity
Predicate	christenedAs	`P179645`	FINISHED
Object	Empress of Canada Empress of Canada was a mid-20th-century Canadian Pacific ocean liner that later served as a popular transatlantic cruise ship.	`E2095200`	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: Empress of Canada | Statement: [SS Empress of Canada (1964), christenedAs, Empress of Canada]

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: Empress of Canada
Triple: [SS Empress of Canada (1964), christenedAs, Empress of Canada]

Generated description

Empress of Canada was a mid-20th-century Canadian Pacific ocean liner that later served as a popular transatlantic cruise ship.

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

Target predicate: christenedAs
Context triple: [SS Empress of Canada (1964), christenedAs, Empress of Canada]

A. christenedBy
Indicates that an entity received its name or was formally dedicated through a christening performed by another entity.
B. christenedDate
Indicates the date on which something was formally named or dedicated, typically in a ceremonial context.
C. coronatedNameAtBirth
Indicates that an entity’s coronation name is the same as their name at birth.
D. usedAsNamesakeFor
Indicates that one entity serves as the source or inspiration for the name given to another entity.
E. changedNameInHonorOf
Indicates that an entity altered its name specifically to honor or pay tribute to another entity.
F. None of above. chosen

Provenance (7 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_69f349c1304081909331872829e38106`	completed	April 30, 2026, 12:23 p.m.
NER	Named-entity recognition	`batch_69f7238172748190b8cd340ad1f4ba80`	completed	May 3, 2026, 10:29 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a370dca12708190a1098b9c19c24b49`	completed	June 20, 2026, 10:01 p.m.
NEDg	Description generation	`batch_6a370e7f2e6c8190858406dcdcdaafb7`	completed	June 20, 2026, 10:04 p.m.
NED2	Entity disambiguation (via description)	`batch_6a370f0b3e5c8190a74b88ad1ba900ea`	completed	June 20, 2026, 10:07 p.m.
PD	Predicate disambiguation	`batch_69f72155c48881909bd40b9aa3febd5a`	completed	May 3, 2026, 10:20 a.m.
PDg	Predicate description generation	`batch_69f72349f1108190b6a06758ab2f40bb`	completed	May 3, 2026, 10:28 a.m.

Created at: May 1, 2026, 1:59 a.m.