Triple

T2472557
Position Surface form Disambiguated ID Type / Status
Subject Pulcheria E55010 entity
Predicate sibling P363 FINISHED
Object Marina
Marina is a female given name of Latin origin, commonly used in various cultures and often associated with the sea.
 E270577 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: [Pulcheria, sibling, Marina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marina
Context triple: [Pulcheria, sibling, 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. Marina
    Marina is the given name of Marina von Neumann Whitman, an American economist and former General Motors executive.
  • C. 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.
  • D. 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.
  • E. 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.
  • 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: [Pulcheria, sibling, Marina]
Generated description
Marina is a female given name of Latin origin, commonly used in various cultures and often associated with the sea.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marina
Target entity description: Marina is a female given name of Latin origin, commonly used in various cultures and often associated with the sea.
  • 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. Marina
    Marina is the given name of Marina von Neumann Whitman, an American economist and former General Motors executive.
  • C. 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.
  • D. 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.
  • E. 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.
  • 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_69ab49e279e88190ab10d7248aea9d11 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd138872481908184e06d3584718e completed March 7, 2026, 7:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69af17a5bb04819090b3156a9819b87d completed March 9, 2026, 6:55 p.m.
NEDg Description generation batch_69af1aca9a5081909e3a1b810b61e19d completed March 9, 2026, 7:08 p.m.
NED2 Entity disambiguation (via description) batch_69af1b45a30c8190a3555fea9c03e343 completed March 9, 2026, 7:11 p.m.
Created at: March 6, 2026, 9:45 p.m.