Triple

T5991401
Position Surface form Disambiguated ID Type / Status
Subject Anna of Russia E133358 entity
Predicate givenName P17 FINISHED
Object Anna
Anna was an 18th-century Empress of Russia from the Romanov dynasty, known for her autocratic rule and the dominance of her German favorites at court.
 E133358 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: Anna | Statement: [Anna of Russia, givenName, Anna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Anna
Context triple: [Anna of Russia, givenName, Anna]
  • A. Anna
    Anna is the given name of Anna Murray Douglass, an African American abolitionist and the first wife of Frederick Douglass.
  • B. Anna
    Anna is a central female character in the comedy Western film "A Million Ways to Die in the West," portrayed as a sharp-shooting, quick-witted woman who helps the protagonist toughen up in the dangerous frontier.
  • C. Anna
    Anna is the given name of pioneering Chinese American actress Anna May Wong, a trailblazing early Hollywood star and fashion icon.
  • D. Anna
    Anna is a character from the video game "Surfacing," likely serving as a key figure in the game's narrative or player interactions.
  • E. Anna
    Anna is a spirited and optimistic princess from Disney's animated film "Frozen," known for her bravery, loyalty, and deep love for her sister Elsa.
  • 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: Anna
Triple: [Anna of Russia, givenName, Anna]
Generated description
Anna was an 18th-century Empress of Russia from the Romanov dynasty, known for her autocratic rule and the dominance of her German favorites at court.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Anna
Target entity description: Anna was an 18th-century Empress of Russia from the Romanov dynasty, known for her autocratic rule and the dominance of her German favorites at court.
  • A. Anna chosen
    Anna was Empress of Russia from 1730 to 1740, known for her autocratic rule and the dominance of her German favorites at court.
  • B. Anna
    Anna is the given name of Anna Laetitia Barbauld, an influential 18th–19th century English poet, essayist, and children's author.
  • C. Anna
    Anna is the tragic, aristocratic heroine of Leo Tolstoy’s novel "Anna Karenina," whose passionate affair and struggle against societal norms lead to her downfall.
  • D. Anna
    Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
  • E. Anna
    Anna is traditionally revered in Christianity as the mother of the Virgin Mary and the grandmother of Jesus.
  • F. None of above.

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_69c0087010d081908bb8142342d63330 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c04e8fd030819095a4f3b3d425ec21 completed March 22, 2026, 8:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c10861c0bc8190b6290d7363f4264a completed March 23, 2026, 9:31 a.m.
NEDg Description generation batch_69c10c44b6408190be8bc1d96e0db2e4 completed March 23, 2026, 9:47 a.m.
NED2 Entity disambiguation (via description) batch_69c10cd7c1c8819085ec8bee7f42afc4 completed March 23, 2026, 9:50 a.m.
Created at: March 22, 2026, 4:05 p.m.