Triple

T14710558
Position Surface form Disambiguated ID Type / Status
Subject Meyer E345534 entity
Predicate hasNotableBearer P458 FINISHED
Object Jürgen Meyer
Jürgen Meyer is a personal name shared by multiple individuals, typically of German origin, and may refer to various notable people in fields such as academia, sports, or the arts.
 E1452345 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: Jürgen Meyer | Statement: [Meyer, hasNotableBearer, Jürgen Meyer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jürgen Meyer
Context triple: [Meyer, hasNotableBearer, Jürgen Meyer]
  • A. Jürgen Doering
    Jürgen Doering is a costume designer known for his work on films such as "Personal Shopper."
  • B. Jürgen Vogel
    Jürgen Vogel is a German actor and producer known for his intense character roles in films such as "The Wave" and "The Free Will."
  • C. Jürgen Schmidt
    Jürgen Schmidt is a personal name shared by multiple individuals, including professionals and public figures in German-speaking countries.
  • D. Jürgen Frank
    Jürgen Frank is a German local politician who serves as the mayor of the municipality of Blindheim in Bavaria.
  • E. Jürgen Büscher
    Jürgen Büscher is a screenwriter best known for co-writing the 1993 German war film "Stalingrad."
  • 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: Jürgen Meyer
Triple: [Meyer, hasNotableBearer, Jürgen Meyer]
Generated description
Jürgen Meyer is a personal name shared by multiple individuals, typically of German origin, and may refer to various notable people in fields such as academia, sports, or the arts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jürgen Meyer
Target entity description: Jürgen Meyer is a personal name shared by multiple individuals, typically of German origin, and may refer to various notable people in fields such as academia, sports, or the arts.
  • A. Jürgen Doering
    Jürgen Doering is a costume designer known for his work on films such as "Personal Shopper."
  • B. Jürgen Vogel
    Jürgen Vogel is a German actor and producer known for his intense character roles in films such as "The Wave" and "The Free Will."
  • C. Jürgen Schmidt
    Jürgen Schmidt is a personal name shared by multiple individuals, including professionals and public figures in German-speaking countries.
  • D. Jürgen Frank
    Jürgen Frank is a German local politician who serves as the mayor of the municipality of Blindheim in Bavaria.
  • E. Jürgen Büscher
    Jürgen Büscher is a screenwriter best known for co-writing the 1993 German war film "Stalingrad."
  • 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_69d822e4a8c08190a155df736bb7bc13 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb9814e0c8190984ac30d276499cc completed April 14, 2026, 10:02 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0900574d988190bd635ebb22c54357 completed May 16, 2026, 11:40 p.m.
NEDg Description generation batch_6a0901a0dac081909f9184e99a70419a completed May 16, 2026, 11:45 p.m.
NED2 Entity disambiguation (via description) batch_6a09023cf27081908eb246367215ae12 completed May 16, 2026, 11:48 p.m.
Created at: April 10, 2026, 1:28 a.m.