Triple

T27130167
Position Surface form Disambiguated ID Type / Status
Subject Goldschmidt E681540 entity
Predicate hasNotableBearer P458 FINISHED
Object Meir Goldschmidt
Meir Goldschmidt was a 19th-century Danish-Jewish writer, journalist, and publisher known for his influential role in Danish literature and for founding the satirical magazine Corsaren.
E1830383 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: Meir Goldschmidt | Statement: [Goldschmidt, hasNotableBearer, Meir Goldschmidt]
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: Meir Goldschmidt
Triple: [Goldschmidt, hasNotableBearer, Meir Goldschmidt]
Generated description
Meir Goldschmidt was a 19th-century Danish-Jewish writer, journalist, and publisher known for his influential role in Danish literature and for founding the satirical magazine Corsaren.

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_69eefacbcc2081909ebf00daa23f1981 completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f62475c1f481908f71234cd4d7012b completed May 2, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1ccf0b77c48190b66e905f1ddc56d3 completed June 1, 2026, 12:15 a.m.
NEDg Description generation batch_6a1ccf8456c8819096402635e1399ba4 completed June 1, 2026, 12:17 a.m.
NED2 Entity disambiguation (via description) batch_6a249466d5b08190bd3886ef517cb367 completed June 6, 2026, 9:43 p.m.
Created at: April 27, 2026, 9:03 a.m.