Triple

T26096639

Position	Surface form	Disambiguated ID	Type / Status
Subject	Vicki Baum	`E658286`	entity
Predicate	notableWork	`P4`	FINISHED
Object	Menschen im Hotel Menschen im Hotel is a 1929 novel by Austrian writer Vicki Baum that interweaves the lives of diverse guests in a luxury Berlin hotel and became internationally famous, inspiring several film adaptations including the classic 1932 movie Grand Hotel.	`E1706725`	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: Menschen im Hotel | Statement: [Vicki Baum, notableWork, Menschen im Hotel]

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: Menschen im Hotel
Triple: [Vicki Baum, notableWork, Menschen im Hotel]

Generated description

Menschen im Hotel is a 1929 novel by Austrian writer Vicki Baum that interweaves the lives of diverse guests in a luxury Berlin hotel and became internationally famous, inspiring several film adaptations including the classic 1932 movie Grand Hotel.

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_6a111b4029c4819084d34feac3f03a1a`	completed	May 23, 2026, 3:13 a.m.
NEDg	Description generation	`batch_6a111c7f25788190ab64d5bd35a691c3`	completed	May 23, 2026, 3:18 a.m.
NED2	Entity disambiguation (via description)	`batch_6a111d2b601881909f79329b949194e5`	completed	May 23, 2026, 3:21 a.m.

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