Triple

T15053679
Position Surface form Disambiguated ID Type / Status
Subject Princess Marianne of Prussia E379431 entity
Predicate givenName P17 FINISHED
Object Anne
Anne is a given name used by Princess Marianne of Prussia, a 19th-century Prussian royal.
E1135401 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: Anne | Statement: [Princess Marianne of Prussia, givenName, Anne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Anne
Context triple: [Princess Marianne of Prussia, givenName, Anne]
  • A. Anne
    Anne is the birth name of Nancy Reagan, the former First Lady of the United States and wife of President Ronald Reagan.
  • B. Anne
    Anne is the protagonist of "The Darkest Hour," around whom the film’s central conflict and emotional journey revolve.
  • C. Anne
    Anne was the ship on which the 17th-century English sailor and later Ceylon captive Robert Knox served during his voyages.
  • D. Anne
    Anne of Palatinate-Simmern was a 16th-century German noblewoman from the House of Wittelsbach who became Electress Palatine through marriage to Elector Frederick III.
  • E. Anne
    Anne is traditionally revered in Christian tradition 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: Anne
Triple: [Princess Marianne of Prussia, givenName, Anne]
Generated description
Anne is a given name used by Princess Marianne of Prussia, a 19th-century Prussian royal.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Anne
Target entity description: Anne is a given name used by Princess Marianne of Prussia, a 19th-century Prussian royal.
  • A. Anne
    Anne is a female given name of Hebrew origin, commonly used in many European languages and historically borne by numerous queens, saints, and notable women.
  • B. Anne
    Anne is a French royal given name borne by Louise Marie Anne de Bourbon, an illegitimate daughter of King Louis XIV of France.
  • C. Anne
    Anne is the given name of Anne Hilarion de Costentin de Tourville, a renowned French naval commander of the late 17th and early 18th centuries.
  • D. Anne
    Anne is the given name of Lady Lucy Anne FitzGerald, an Irish noblewoman of the late 18th and early 19th centuries known for her connections to the United Irishmen movement.
  • E. Anne
    Anne is the given name of Anne Morrow Lindbergh, the American author and aviator who was married to famed aviator Charles Lindbergh.
  • 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_69d85cd64d108190853797a95c11cc45 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69deda92091c81909180f486edf01405 completed April 15, 2026, 12:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69fea5bdeee48190949b0fe63eb6a21a completed May 9, 2026, 3:10 a.m.
NEDg Description generation batch_69fea79dd1bc8190ae1ac5edad3db9cb completed May 9, 2026, 3:18 a.m.
NED2 Entity disambiguation (via description) batch_69fea83aaff48190af7a7399e40fdf46 completed May 9, 2026, 3:21 a.m.
Created at: April 10, 2026, 3:01 a.m.