Triple

T24937183
Position Surface form Disambiguated ID Type / Status
Subject Theodor Mundt E623346 entity
Predicate notable work P4 FINISHED
Object Die Kunst der deutschen Prosa
Die Kunst der deutschen Prosa is a 19th-century literary study by Theodor Mundt that analyzes and theorizes the stylistic principles of German prose.
 E1656277 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: Die Kunst der deutschen Prosa | Statement: [Theodor Mundt, notable work, Die Kunst der deutschen Prosa]
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: Die Kunst der deutschen Prosa
Triple: [Theodor Mundt, notable work, Die Kunst der deutschen Prosa]
Generated description
Die Kunst der deutschen Prosa is a 19th-century literary study by Theodor Mundt that analyzes and theorizes the stylistic principles of German prose.

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_69e2fac6b5a48190a1c38857f00915a9 completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f423d69ecc8190938ae3933ba0eb82 completed May 1, 2026, 3:53 a.m.
NED1 Entity disambiguation (via context triple) batch_6a103340e9e8819095238a51efedf38e completed May 22, 2026, 10:43 a.m.
NEDg Description generation batch_6a1033eeacac81909e208f3b3e17190e completed May 22, 2026, 10:46 a.m.
NED2 Entity disambiguation (via description) batch_6a1034c45fb88190865f904fd8e766b3 completed May 22, 2026, 10:49 a.m.
Created at: April 18, 2026, 5:30 a.m.