Triple

T77492
Position Surface form Disambiguated ID Type / Status
Subject Detlev W. Bronk E1549 entity
Predicate givenName P17 FINISHED
Object Detlev
Detlev is a masculine given name of German origin, historically borne by several notable figures in science and academia.
 E23265 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: Detlev | Statement: [Detlev W. Bronk, givenName, Detlev]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Detlev
Context triple: [Detlev W. Bronk, givenName, Detlev]
  • A. Erwin
    Erwin is a masculine given name of German origin, historically associated with figures such as the World War II field marshal Erwin Rommel.
  • B. Johan
    Johan is the given first name of J. Erik Jonsson, an American businessman and philanthropist who co-founded Texas Instruments and served as mayor of Dallas.
  • C. Olaf Kölzig
    Olaf Kölzig is a former German-Canadian NHL goaltender best known for his long, standout career with the Washington Capitals, including winning the Vezina Trophy in 2000.
  • D. Adelbert
    Adelbert is a masculine given name of German origin, historically borne by figures such as the poet and naturalist Adelbert von Chamisso.
  • E. Theodor
    Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
  • 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: Detlev
Triple: [Detlev W. Bronk, givenName, Detlev]
Generated description
Detlev is a masculine given name of German origin, historically borne by several notable figures in science and academia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Detlev
Target entity description: Detlev is a masculine given name of German origin, historically borne by several notable figures in science and academia.
  • A. Erwin
    Erwin is a masculine given name of German origin, historically associated with figures such as the World War II field marshal Erwin Rommel.
  • B. Johan
    Johan is the given first name of J. Erik Jonsson, an American businessman and philanthropist who co-founded Texas Instruments and served as mayor of Dallas.
  • C. Olaf Kölzig
    Olaf Kölzig is a former German-Canadian NHL goaltender best known for his long, standout career with the Washington Capitals, including winning the Vezina Trophy in 2000.
  • D. Adelbert
    Adelbert is a masculine given name of German origin, historically borne by figures such as the poet and naturalist Adelbert von Chamisso.
  • E. Theodor
    Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
  • 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_69a24c60d19c8190a1b6c105ca59ef5b completed Feb. 28, 2026, 2:01 a.m.
NER Named-entity recognition batch_69a24f1d20b88190b66836cc018e52e1 completed Feb. 28, 2026, 2:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2fcfeaae88190987696a6a6dafe12 completed Feb. 28, 2026, 2:34 p.m.
NEDg Description generation batch_69a2fd4d08588190b5a7c5ca435bac42 completed Feb. 28, 2026, 2:35 p.m.
NED2 Entity disambiguation (via description) batch_69a2fdfceb20819097ee6c75234bd252 completed Feb. 28, 2026, 2:38 p.m.
Created at: Feb. 28, 2026, 2:06 a.m.