Triple

T38003452

Position	Surface form	Disambiguated ID	Type / Status
Subject	Redpath Sugar Refinery	`E948166`	entity
Predicate	hasPart	`P35`	FINISHED
Object	Redpath Sugar Museum The Redpath Sugar Museum is a small industrial heritage museum in Toronto that showcases the history of the Redpath Sugar company and the sugar industry in Canada.	`E2251209`	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: Redpath Sugar Museum | Statement: [Redpath Sugar Refinery, hasPart, Redpath Sugar Museum]

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: Redpath Sugar Museum
Triple: [Redpath Sugar Refinery, hasPart, Redpath Sugar Museum]

Generated description

The Redpath Sugar Museum is a small industrial heritage museum in Toronto that showcases the history of the Redpath Sugar company and the sugar industry in Canada.

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_69f76efb4b10819092c8c2ba28ac06a8`	completed	May 3, 2026, 3:51 p.m.
NER	Named-entity recognition	`batch_69fbc93cc37481909d4ceca6bec23ebb`	completed	May 6, 2026, 11:05 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a412cc536f481909dfd7610d64086a3`	completed	June 28, 2026, 2:16 p.m.
NEDg	Description generation	`batch_6a41324e591481908f5aef57c6329553`	completed	June 28, 2026, 2:40 p.m.
NED2	Entity disambiguation (via description)	`batch_6a41346459e48190a9f83a5d2cbb3c98`	completed	June 28, 2026, 2:49 p.m.

Created at: May 3, 2026, 4:20 p.m.