Triple

T26096791

Position	Surface form	Disambiguated ID	Type / Status
Subject	General Director Preysing	`E658291`	entity
Predicate	settingOfActivity	`P1957`	FINISHED
Object	Grand Hotel in Berlin Grand Hotel in Berlin is a fictional luxury hotel that serves as the central setting for Vicki Baum’s novel and its famous stage and film adaptations, where the intersecting lives of diverse guests unfold.	`E272482`	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: Grand Hotel in Berlin | Statement: [General Director Preysing, settingOfActivity, Grand Hotel in Berlin]

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: Grand Hotel in Berlin
Triple: [General Director Preysing, settingOfActivity, Grand Hotel in Berlin]

Generated description

Grand Hotel in Berlin is a fictional luxury hotel that serves as the central setting for Vicki Baum’s novel and its famous stage and film adaptations, where the intersecting lives of diverse guests unfold.

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_69ee5bbfc4d08190a1b206d0ac3a1e8d`	completed	April 26, 2026, 6:38 p.m.
NER	Named-entity recognition	`batch_69f6073720748190aecfc9af3ba039fd`	completed	May 2, 2026, 2:16 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a1185620a5881908d2a7b88d4b1a302`	completed	May 23, 2026, 10:45 a.m.
NEDg	Description generation	`batch_6a11865aaac881909aa388f473a6e5a3`	completed	May 23, 2026, 10:50 a.m.
NED2	Entity disambiguation (via description)	`batch_6a11873fe9708190a0ad2b27028b120a`	completed	May 23, 2026, 10:53 a.m.

Created at: April 26, 2026, 7:51 p.m.