Triple

T2063280
Position Surface form Disambiguated ID Type / Status
Subject Marina von Neumann Whitman E45839 entity
Predicate givenName P17 FINISHED
Object Marina
Marina is the given name of Marina von Neumann Whitman, an American economist and former General Motors executive.
 E228613 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: Marina | Statement: [Marina von Neumann Whitman, givenName, Marina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marina
Context triple: [Marina von Neumann Whitman, givenName, Marina]
  • A. Marina
    Marina is a recurring comedic character in the long-running British sitcom "Last of the Summer Wine," known for her flirtatious relationship with the married Howard.
  • B. Lissa
    Lissa is a historic town in western Poland, known today as Leszno, that was once part of Germany and is notable as the birthplace of several prominent Jewish and intellectual figures.
  • C. Theodosia
    Theodosia is a historic port city on the southeastern coast of Crimea, known for its long history as a trading center on the Black Sea.
  • D. Malaya Nevka
    Malaya Nevka is a distributary channel of the Neva River in Saint Petersburg, Russia, forming part of the city’s intricate river and canal network.
  • E. Thalassa
    Thalassa is a small, inner irregularly shaped moon of Neptune that orbits close to the planet within its ring system.
  • 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: Marina
Triple: [Marina von Neumann Whitman, givenName, Marina]
Generated description
Marina is the given name of Marina von Neumann Whitman, an American economist and former General Motors executive.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marina
Target entity description: Marina is the given name of Marina von Neumann Whitman, an American economist and former General Motors executive.
  • A. Marina
    Marina is a recurring comedic character in the long-running British sitcom "Last of the Summer Wine," known for her flirtatious relationship with the married Howard.
  • B. Lissa
    Lissa is a historic town in western Poland, known today as Leszno, that was once part of Germany and is notable as the birthplace of several prominent Jewish and intellectual figures.
  • C. Theodosia
    Theodosia is a historic port city on the southeastern coast of Crimea, known for its long history as a trading center on the Black Sea.
  • D. Malaya Nevka
    Malaya Nevka is a distributary channel of the Neva River in Saint Petersburg, Russia, forming part of the city’s intricate river and canal network.
  • E. Thalassa
    Thalassa is a small, inner irregularly shaped moon of Neptune that orbits close to the planet within its ring system.
  • 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_69a8891b38288190abd572ccad9b6928 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb9d3a92081909416c1d087876e99 completed March 7, 2026, 5:38 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae2018ca808190a7c4586364e3587d completed March 9, 2026, 1:19 a.m.
NEDg Description generation batch_69ae20cbf3608190984dfd9638b9053b completed March 9, 2026, 1:22 a.m.
NED2 Entity disambiguation (via description) batch_69ae213410708190af9715f488c5b8f5 completed March 9, 2026, 1:24 a.m.
Created at: March 4, 2026, 7:40 p.m.