Triple

T1138116
Position Surface form Disambiguated ID Type / Status
Subject Anna Seward E23185 entity
Predicate givenName P17 FINISHED
Object Anna
Anna is a feminine given name of Hebrew origin meaning "grace" or "favor," widely used across many cultures and languages.
 E161036 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 Seward, givenName, Anna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Anna
Context triple: [Anna Seward, 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 character from the video game "Surfacing," likely serving as a key figure in the game's narrative or player interactions.
  • C. Anna
    Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
  • D. 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.
  • E. Anna
    Anna was Empress of Russia from 1730 to 1740, known for her autocratic rule and the dominance of her German favorites at court.
  • 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 Seward, givenName, Anna]
Generated description
Anna is a feminine given name of Hebrew origin meaning "grace" or "favor," widely used across many cultures and languages.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Anna
Target entity description: Anna is a feminine given name of Hebrew origin meaning "grace" or "favor," widely used across many cultures and languages.
  • 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 pioneering Chinese American actress Anna May Wong, a trailblazing early Hollywood star and fashion icon.
  • C. Anna
    Anna is the given name of Anna Murray Douglass, an African American abolitionist and the first wife of Frederick Douglass.
  • D. Anna
    Anna was Empress of Russia from 1730 to 1740, known for her autocratic rule and the dominance of her German favorites at court.
  • E. 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.
  • 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_69a493ec75988190b63a11bafaec29b4 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4bc25dda481909a26d726fdbdbb50 completed March 1, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69ace54f52988190b25c35271721c3ee completed March 8, 2026, 2:56 a.m.
NEDg Description generation batch_69ace5db553c8190b0d09462411f3dcf completed March 8, 2026, 2:58 a.m.
NED2 Entity disambiguation (via description) batch_69ace647c04881908ab550505110c29b completed March 8, 2026, 3 a.m.
Created at: March 1, 2026, 7:44 p.m.