Triple

T31175257
Position Surface form Disambiguated ID Type / Status
Subject Nandlstadt E794725 entity
Predicate hasCulturalRegion P1968 FINISHED
Object Old Bavarian region
The Old Bavarian region is a historical-cultural area in southeastern Germany traditionally associated with the core territories and dialects of the former Duchy and later Kingdom of Bavaria.
 E74080 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: Old Bavarian region | Statement: [Nandlstadt, hasCulturalRegion, Old Bavarian region]
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: Old Bavarian region
Triple: [Nandlstadt, hasCulturalRegion, Old Bavarian region]
Generated description
The Old Bavarian region is a historical-cultural area in southeastern Germany traditionally associated with the core territories and dialects of the former Duchy and later Kingdom of Bavaria.

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_69f224d5b9708190b6ca79ad2fd3a28a completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f698b3e96081909996ecea317507a4 completed May 3, 2026, 12:37 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2947349288819081992684ec970ca9 completed June 10, 2026, 11:15 a.m.
NEDg Description generation batch_6a2947e26f408190a9bc961974014450 completed June 10, 2026, 11:17 a.m.
NED2 Entity disambiguation (via description) batch_6a294eee82d48190a23f96b28727b881 completed June 10, 2026, 11:47 a.m.
Created at: April 29, 2026, 9:07 p.m.