Triple

T17177509
Position Surface form Disambiguated ID Type / Status
Subject Todtnau E416898 entity
Predicate subdivision P747 FINISHED
Object Präg
Präg is a small village in the Black Forest region of Baden-Württemberg, Germany, known for its mountainous landscape and outdoor recreation opportunities.
E1254240 NE FINISHED

How this triple was built (4 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: Präg | Statement: [Todtnau, subdivision, Präg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Präg
Context triple: [Todtnau, subdivision, Präg]
  • A. Bavier
    Bavier is the surname of Frances Bavier, the American actress best known for playing Aunt Bee on the classic television series "The Andy Griffith Show."
  • B. Rentenmark
    The Rentenmark was a temporary German currency introduced in 1923 to halt hyperinflation and stabilize the economy during the Weimar Republic.
  • C. Schwarzenberg
    Schwarzenberg is the noble family name of a prominent Central European princely house historically influential in Austrian and Bohemian politics and military affairs.
  • D. Schwarzenberg
    Schwarzenberg is a locality situated within Germany’s Murgtal valley region.
  • E. Ostmark
    Ostmark was the name used by Nazi Germany for annexed Austria, functioning as an administrative region of the Third Reich.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Präg
Triple: [Todtnau, subdivision, Präg]
Generated description
Präg is a small village in the Black Forest region of Baden-Württemberg, Germany, known for its mountainous landscape and outdoor recreation opportunities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Präg
Target entity description: Präg is a small village in the Black Forest region of Baden-Württemberg, Germany, known for its mountainous landscape and outdoor recreation opportunities.
  • A. Bavier
    Bavier is the surname of Frances Bavier, the American actress best known for playing Aunt Bee on the classic television series "The Andy Griffith Show."
  • B. Rentenmark
    The Rentenmark was a temporary German currency introduced in 1923 to halt hyperinflation and stabilize the economy during the Weimar Republic.
  • C. Schwarzenberg
    Schwarzenberg is the noble family name of a prominent Central European princely house historically influential in Austrian and Bohemian politics and military affairs.
  • D. Schwarzenberg
    Schwarzenberg is a locality situated within Germany’s Murgtal valley region.
  • E. Ostmark
    Ostmark was the name used by Nazi Germany for annexed Austria, functioning as an administrative region of the Third Reich.
  • F. None of above. chosen

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_69d886d5f34c8190b24564dfaa63f3fb completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3fc0ee5008190a73875b39841fd9f completed April 18, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0148455afc8190931ba1316705ae0c completed May 11, 2026, 3:08 a.m.
NEDg Description generation batch_6a014a2f8fec8190b1303967a76ceb63 completed May 11, 2026, 3:17 a.m.
NED2 Entity disambiguation (via description) batch_6a014ab0be388190a6ce49cff469fe81 completed May 11, 2026, 3:19 a.m.
Created at: April 10, 2026, 5:37 a.m.