Triple

T81402
Position Surface form Disambiguated ID Type / Status
Subject Oskar Morgenstern E1635 entity
Predicate givenName P17 FINISHED
Object Oskar
Oskar is a masculine given name of Germanic origin, commonly used in various European countries.
 E21155 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: Oskar | Statement: [Oskar Morgenstern, givenName, Oskar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Oskar
Context triple: [Oskar Morgenstern, givenName, Oskar]
  • 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. Theodor
    Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
  • C. Otto
    Otto is the title of one of the early nominative reports that were later incorporated into the official United States Reports, documenting decisions of the U.S. Supreme Court.
  • D. 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.
  • E. Herbert
    Herbert is a masculine given name of Germanic origin that has been borne by various notable figures, including U.S. President Herbert Hoover.
  • 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: Oskar
Triple: [Oskar Morgenstern, givenName, Oskar]
Generated description
Oskar is a masculine given name of Germanic origin, commonly used in various European countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Oskar
Target entity description: Oskar is a masculine given name of Germanic origin, commonly used in various European countries.
  • 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. Theodor
    Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
  • C. Otto
    Otto is the title of one of the early nominative reports that were later incorporated into the official United States Reports, documenting decisions of the U.S. Supreme Court.
  • D. 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.
  • E. Herbert
    Herbert is a masculine given name of Germanic origin that has been borne by various notable figures, including U.S. President Herbert Hoover.
  • 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_69a24f354d088190972791051d2d99f8 completed Feb. 28, 2026, 2:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2e3bbb3508190870d3291a836923e completed Feb. 28, 2026, 12:46 p.m.
NEDg Description generation batch_69a2e41986ec819089095e6843907cd4 completed Feb. 28, 2026, 12:48 p.m.
NED2 Entity disambiguation (via description) batch_69a2e4c29a208190afb2247950c86c59 completed Feb. 28, 2026, 12:51 p.m.
Created at: Feb. 28, 2026, 2:06 a.m.