Triple

T38413981
Position Surface form Disambiguated ID Type / Status
Subject Dresden dialect E901553 entity
Predicate distinguishedFrom P1612 FINISHED
Object Chemnitz dialect
The Chemnitz dialect is a regional variety of the German language spoken around the city of Chemnitz in Saxony, characterized by its own distinct phonetic and lexical features within the Upper Saxon dialect group.
E2269444 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: Chemnitz dialect | Statement: [Dresden dialect, distinguishedFrom, Chemnitz dialect]
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: Chemnitz dialect
Triple: [Dresden dialect, distinguishedFrom, Chemnitz dialect]
Generated description
The Chemnitz dialect is a regional variety of the German language spoken around the city of Chemnitz in Saxony, characterized by its own distinct phonetic and lexical features within the Upper Saxon dialect group.

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_69f76e61e79c81908b787d83b46ab92b completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69fccd6665388190995223f7af273ecd completed May 7, 2026, 5:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41c283e5948190b8b9cfdd3633b21d completed June 29, 2026, 12:55 a.m.
NEDg Description generation batch_6a41c3dac6ec81909231576db0b9808d completed June 29, 2026, 1:01 a.m.
NED2 Entity disambiguation (via description) batch_6a41c481f72c8190b44745166b1bb8c4 completed June 29, 2026, 1:04 a.m.
Created at: May 3, 2026, 4:31 p.m.