Triple

T778196
Position Surface form Disambiguated ID Type / Status
Subject Anna of Russia E16436 entity
Predicate givenName P17 FINISHED
Object Anna
Anna was Empress of Russia from 1730 to 1740, 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 the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
  • C. 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.
  • D. Amy
    Amy is a critically acclaimed 2015 documentary film about the life and career of British singer-songwriter Amy Winehouse.
  • E. Ann
    Ann is a given name commonly used as a feminine first or middle name in English-speaking countries.
  • 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 Empress of Russia from 1730 to 1740, 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 Empress of Russia from 1730 to 1740, known for her autocratic rule and the dominance of her German favorites at court.
  • A. Anna
    Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
  • B. Anna
    Anna is the given name of Anna Murray Douglass, an African American abolitionist and the first wife of Frederick Douglass.
  • C. 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.
  • D. Amy
    Amy is a critically acclaimed 2015 documentary film about the life and career of British singer-songwriter Amy Winehouse.
  • E. Ann
    Ann is a given name commonly used as a feminine first or middle name in English-speaking countries.
  • 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_69a4936ad1fc81908f190208059ccf78 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a74f886081909c27b786e3adbe32 completed March 1, 2026, 8:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac6600fa4c8190be934f49ba4b75ca completed March 7, 2026, 5:53 p.m.
NEDg Description generation batch_69ac669c3d7c819085194c797d41fb5d completed March 7, 2026, 5:55 p.m.
NED2 Entity disambiguation (via description) batch_69ac670ce8b881909d4ea8082f7fe096 completed March 7, 2026, 5:57 p.m.
Created at: March 1, 2026, 7:37 p.m.