Triple

T13022911
Position Surface form Disambiguated ID Type / Status
Subject Landgravine of Hesse-Homburg E326221 entity
Predicate givenName P17 FINISHED
Object Elizabeth
Elizabeth was a German noblewoman who held the title of Landgravine of Hesse-Homburg.
E1015505 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: Elizabeth | Statement: [Landgravine of Hesse-Homburg, givenName, Elizabeth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elizabeth
Context triple: [Landgravine of Hesse-Homburg, givenName, Elizabeth]
  • A. Elizabeth
    Elizabeth is the formal first name of Bess Truman, who served as First Lady of the United States as the wife of President Harry S. Truman.
  • B. Elizabeth
    Elizabeth is the middle name of Lady Sarah Chatto, a British painter and member of the extended royal family.
  • C. Elizabeth
    Elizabeth is the central protagonist of the interactive narrative game "If/Then," around whom the story’s key choices and emotional developments revolve.
  • D. Elizabeth
    Elizabeth is the central character in the Broadway musical "If/Then," a woman who explores how a single choice can lead to radically different life paths.
  • E. Elizabeth
    Elizabeth is the given name of Elizabeth Jane Cochrane, better known as pioneering American investigative journalist Nellie Bly.
  • 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: Elizabeth
Triple: [Landgravine of Hesse-Homburg, givenName, Elizabeth]
Generated description
Elizabeth was a German noblewoman who held the title of Landgravine of Hesse-Homburg.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Elizabeth
Target entity description: Elizabeth was a German noblewoman who held the title of Landgravine of Hesse-Homburg.
  • A. Elizabeth
    Elizabeth was a Greek and Danish princess of the early 20th century, born into the royal families of both Greece and Denmark.
  • B. Elizabeth
    Elizabeth was the Duchess of York who later became Queen Elizabeth The Queen Mother, a prominent member of the British royal family in the 20th century.
  • C. Elizabeth
    Elizabeth was a medieval English noblewoman, the daughter of John of Gaunt and granddaughter of King Edward III.
  • D. Elizabeth
    Elizabeth of Hesse and by Rhine was a German princess who became a Russian Orthodox nun and martyr, renowned for her charitable work and tragic death following the Russian Revolution.
  • E. Elizabeth
    Elizabeth is the given name of Lady Elizabeth Spencer-Churchill, a member of the prominent Spencer-Churchill aristocratic family in Britain.
  • 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_69d8076cc45c81908123123f43e69266 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97ed05e9c8190a4f208662bca0602 completed April 10, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6c0ec7e8081909fcff6cff11a9337 completed May 3, 2026, 3:28 a.m.
NEDg Description generation batch_69f6c30cf4f88190b0f2cf958df043c0 completed May 3, 2026, 3:37 a.m.
NED2 Entity disambiguation (via description) batch_69f6c38bc0b08190b76cb0853d99ad82 completed May 3, 2026, 3:39 a.m.
Created at: April 9, 2026, 8:52 p.m.