Triple

T511614
Position Surface form Disambiguated ID Type / Status
Subject Rudolf Carnap E10621 entity
Predicate birthPlace P1 FINISHED
Object Ronsdorf
Ronsdorf is a district of the German city of Wuppertal in North Rhine-Westphalia, historically known as an independent town in the Bergisches Land region.
 E78956 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: Ronsdorf | Statement: [Rudolf Carnap, birthPlace, Ronsdorf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ronsdorf
Context triple: [Rudolf Carnap, birthPlace, Ronsdorf]
  • A. Rhöndorf
    Rhöndorf is a district of Bad Honnef in Germany, best known as the longtime residence and final home of the first Chancellor of the Federal Republic of Germany, Konrad Adenauer.
  • B. Schöneberg
    Schöneberg is a district of Berlin, Germany, historically notable as the site of John F. Kennedy’s famous “Ich bin ein Berliner” speech.
  • C. Boblingen
    Böblingen is a town in the German state of Baden-Württemberg, known for its automotive industry presence and proximity to Stuttgart.
  • D. Lünen
    Lünen is a town in North Rhine-Westphalia, Germany, known as an industrial and commuter city in the Ruhr area.
  • E. Lichtenfels
    Lichtenfels is a town in the Upper Franconia region of Bavaria, Germany, known for its basket-making tradition and historic architecture.
  • 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: Ronsdorf
Triple: [Rudolf Carnap, birthPlace, Ronsdorf]
Generated description
Ronsdorf is a district of the German city of Wuppertal in North Rhine-Westphalia, historically known as an independent town in the Bergisches Land region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ronsdorf
Target entity description: Ronsdorf is a district of the German city of Wuppertal in North Rhine-Westphalia, historically known as an independent town in the Bergisches Land region.
  • A. Rhöndorf
    Rhöndorf is a district of Bad Honnef in Germany, best known as the longtime residence and final home of the first Chancellor of the Federal Republic of Germany, Konrad Adenauer.
  • B. Schöneberg
    Schöneberg is a district of Berlin, Germany, historically notable as the site of John F. Kennedy’s famous “Ich bin ein Berliner” speech.
  • C. Boblingen
    Böblingen is a town in the German state of Baden-Württemberg, known for its automotive industry presence and proximity to Stuttgart.
  • D. Lünen
    Lünen is a town in North Rhine-Westphalia, Germany, known as an industrial and commuter city in the Ruhr area.
  • E. Lichtenfels
    Lichtenfels is a town in the Upper Franconia region of Bavaria, Germany, known for its basket-making tradition and historic architecture.
  • 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_69a2e84a0d08819087e01863fcd9abf1 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2f16768c081909d05537ff070868b completed Feb. 28, 2026, 1:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5692e9f248190945be16aac260038 completed March 2, 2026, 10:40 a.m.
NEDg Description generation batch_69a56ae958f481909b097848ef1d9b5d completed March 2, 2026, 10:48 a.m.
NED2 Entity disambiguation (via description) batch_69a56b86d684819080c398847af0551b completed March 2, 2026, 10:50 a.m.
Created at: Feb. 28, 2026, 1:12 p.m.