Triple

T2351207
Position Surface form Disambiguated ID Type / Status
Subject Lady Macbeth E47451 entity
Predicate character P662 FINISHED
Object Anna
Anna is an actress known for portraying the ambitious and manipulative Lady Macbeth in a production of Shakespeare’s tragedy "Macbeth."
 E297177 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: [Lady Macbeth, character, Anna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Anna
Context triple: [Lady Macbeth, character, Anna]
  • 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. Anna
    Anna is the given name of Anna Laetitia Barbauld, an influential 18th–19th century English poet, essayist, and children's author.
  • E. Anna
    Anna is traditionally revered in Christianity as the mother of the Virgin Mary and the grandmother of Jesus.
  • 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: [Lady Macbeth, character, Anna]
Generated description
Anna is an actress known for portraying the ambitious and manipulative Lady Macbeth in a production of Shakespeare’s tragedy "Macbeth."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Anna
Target entity description: Anna is an actress known for portraying the ambitious and manipulative Lady Macbeth in a production of Shakespeare’s tragedy "Macbeth."
  • A. Anna
    Anna is a key female resistance fighter in the World War II adventure film "The Guns of Navarone," whose complex loyalties and actions significantly impact the mission’s outcome.
  • 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 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. 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.
  • 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

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_69a88a1b678c8190bce986922ba60ce0 completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abc6f75d888190a2e41edaa532e83f completed March 7, 2026, 6:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc01d9b40819089e6a59164e64949 completed March 10, 2026, 6:54 a.m.
NEDg Description generation batch_69afc1317a648190b8c7fb834a952a95 completed March 10, 2026, 6:58 a.m.
NED2 Entity disambiguation (via description) batch_69afc1a64e608190baadbf499e35e73e completed March 10, 2026, 7 a.m.
Created at: March 4, 2026, 7:54 p.m.