Triple

T26758057
Position Surface form Disambiguated ID Type / Status
Subject Exilliteratur E674724 entity
Predicate wichtigeAutoren P12787 FINISHED
Object Hans Marchwitza
Hans Marchwitza was a German worker-writer and communist author known for his socially critical, proletarian novels and his role in anti-fascist exile literature.
E2290042 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: Hans Marchwitza | Statement: [Exilliteratur, wichtigeAutoren, Hans Marchwitza]
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: Hans Marchwitza
Triple: [Exilliteratur, wichtigeAutoren, Hans Marchwitza]
Generated description
Hans Marchwitza was a German worker-writer and communist author known for his socially critical, proletarian novels and his role in anti-fascist exile literature.

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_69eecda6e9dc81908452fab3ba17ed9b completed April 27, 2026, 2:44 a.m.
NER Named-entity recognition batch_69f64cb2b5f4819092e363d5076cddbb completed May 2, 2026, 7:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a5b8f63ad508190990be82868aa8bce completed July 18, 2026, 2:36 p.m.
NEDg Description generation batch_6a5b8fd26dbc8190808c98588bfa1411 completed July 18, 2026, 2:38 p.m.
NED2 Entity disambiguation (via description) batch_6a5b907dddc48190956e9497549a618f completed July 18, 2026, 2:41 p.m.
Created at: April 27, 2026, 3:56 a.m.