Triple

T25160256
Position Surface form Disambiguated ID Type / Status
Subject Nuremberg school E626416 entity
Predicate hasPart P35 FINISHED
Object Hans Leonhard Schäufelein
Hans Leonhard Schäufelein was a German painter and woodcut designer of the early 16th century, known as a pupil of Albrecht Dürer and a prominent representative of the Nuremberg school.
E2287730 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 Leonhard Schäufelein | Statement: [Nuremberg school, hasPart, Hans Leonhard Schäufelein]
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 Leonhard Schäufelein
Triple: [Nuremberg school, hasPart, Hans Leonhard Schäufelein]
Generated description
Hans Leonhard Schäufelein was a German painter and woodcut designer of the early 16th century, known as a pupil of Albrecht Dürer and a prominent representative of the Nuremberg school.

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_69e2ff2834ec8190b0872e2ec3d76023 completed April 18, 2026, 3:48 a.m.
NER Named-entity recognition batch_69f46b8bc80081909a48236997f4018d completed May 1, 2026, 8:59 a.m.
NED1 Entity disambiguation (via context triple) batch_6a5a106459008190b879d5ddf8573180 completed July 17, 2026, 11:22 a.m.
NEDg Description generation batch_6a5a13f1c6b88190afa677276ce02294 completed July 17, 2026, 11:37 a.m.
NED2 Entity disambiguation (via description) batch_6a5a14e06cd08190bbd5bf2865cfe83c completed July 17, 2026, 11:41 a.m.
Created at: April 18, 2026, 6:31 a.m.