Triple

T18686170
Position Surface form Disambiguated ID Type / Status
Subject Elizabeth of Hungary, Duchess of Bavaria E456869 entity
Predicate givenName P17 FINISHED
Object Elizabeth
Elizabeth was a medieval noblewoman who held the title of Duchess of Bavaria and was known as Elizabeth of Hungary.
E1337086 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: [Elizabeth of Hungary, Duchess of Bavaria, givenName, Elizabeth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elizabeth
Context triple: [Elizabeth of Hungary, Duchess of Bavaria, givenName, Elizabeth]
  • A. Elizabeth
    Elizabeth is a comedic, high-strung fiancée character in the 1974 Mel Brooks film "Young Frankenstein," known for her dramatic personality and memorable scenes.
  • B. Elizabeth
    Elizabeth is a central character in the 1931 horror film "Frankenstein," serving as Henry Frankenstein’s fiancée and a key figure whose vulnerability heightens the story’s emotional and dramatic stakes.
  • C. 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.
  • D. Elizabeth
    Elizabeth is the given name of Elizabeth Jane Cochrane, better known as pioneering American investigative journalist Nellie Bly.
  • E. Elizabeth
    Elizabeth of Denmark was a 16th-century Danish princess who became Electress of Brandenburg through her marriage to Joachim II Hector.
  • 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: [Elizabeth of Hungary, Duchess of Bavaria, givenName, Elizabeth]
Generated description
Elizabeth was a medieval noblewoman who held the title of Duchess of Bavaria and was known as Elizabeth of Hungary.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Elizabeth
Target entity description: Elizabeth was a medieval noblewoman who held the title of Duchess of Bavaria and was known as Elizabeth of Hungary.
  • A. Elizabeth
    Elizabeth was a German noblewoman who held the title of Landgravine of Hesse-Homburg.
  • B. Elizabeth
    Elizabeth was a medieval English noblewoman, the daughter of John of Gaunt and granddaughter of King Edward III.
  • C. 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.
  • D. Elizabeth
    Elizabeth of Aragon, also known as Saint Elizabeth of Portugal, was a 13th–14th century queen consort renowned for her piety, charity, and role as a peacemaker in dynastic conflicts.
  • E. Elizabeth
    Elizabeth was a Greek and Danish princess of the early 20th century, born into the royal families of both Greece and Denmark.
  • 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_69d8d391eb488190ac2e9abf5bf255e4 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e55b2d9a24819098c8e963ee430437 completed April 19, 2026, 10:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a05235a0d488190a3a5d01822f45e08 completed May 14, 2026, 1:20 a.m.
NEDg Description generation batch_6a05248c12d88190abb947a37b7d180c completed May 14, 2026, 1:25 a.m.
NED2 Entity disambiguation (via description) batch_6a052523e1588190a365dc093d8352a1 completed May 14, 2026, 1:28 a.m.
Created at: April 10, 2026, 11:49 a.m.