Triple

T11523142
Position Surface form Disambiguated ID Type / Status
Subject Princess Wilhelmina Caroline of Denmark E273217 entity
Predicate givenName P17 FINISHED
Object Caroline
Caroline is a Danish princess, known formally as Princess Wilhelmina Caroline of Denmark, who lived in the 18th century.
 E930082 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: Caroline | Statement: [Princess Wilhelmina Caroline of Denmark, givenName, Caroline]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Caroline
Context triple: [Princess Wilhelmina Caroline of Denmark, givenName, Caroline]
  • A. Caroline
    Caroline is a rural town in Tompkins County, New York, known for its small communities, scenic landscapes, and proximity to the city of Ithaca.
  • B. Caroline
    Caroline was a British princess of the early 18th century, the daughter of King George II and Queen Caroline of Ansbach.
  • C. Caroline
    Caroline is a feminine given name of French and Latin origin, commonly used in English-speaking and European countries.
  • D. Caroline
    Caroline of Ansbach was an 18th-century Queen consort of Great Britain and Ireland as the wife of King George II, noted for her political influence and patronage of the arts and sciences.
  • E. Caroline
    Caroline is the given name of Lady Caroline Spencer-Churchill, a British aristocrat from the prominent Spencer-Churchill family.
  • 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: Caroline
Triple: [Princess Wilhelmina Caroline of Denmark, givenName, Caroline]
Generated description
Caroline is a Danish princess, known formally as Princess Wilhelmina Caroline of Denmark, who lived in the 18th century.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Caroline
Target entity description: Caroline is a Danish princess, known formally as Princess Wilhelmina Caroline of Denmark, who lived in the 18th century.
  • A. Caroline
    Caroline was a British princess of the early 18th century, the daughter of King George II and Queen Caroline of Ansbach.
  • B. Caroline
    Caroline of Ansbach was an 18th-century Queen consort of Great Britain and Ireland as the wife of King George II, noted for her political influence and patronage of the arts and sciences.
  • C. Caroline
    Caroline is the given name of Lady Caroline Spencer-Churchill, a British aristocrat from the prominent Spencer-Churchill family.
  • D. Caroline
    Caroline von Humboldt was a German salonnière, art patron, and intellectual known for her influential role in Berlin’s cultural and scholarly life in the early 19th century.
  • E. Caroline
    Caroline is a feminine given name of French and Latin origin, commonly used in English-speaking and European countries.
  • 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_69d6aae3fbec8190a14632a5df2538b6 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d87fd26648819083de19bcddf8ad69 completed April 10, 2026, 4:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69e62551a36081908ef6418fe8e2155c completed April 20, 2026, 1:08 p.m.
NEDg Description generation batch_69e62cf5b9988190bc1935993f0111f7 completed April 20, 2026, 1:41 p.m.
NED2 Entity disambiguation (via description) batch_69e66433ddb48190994bb1160b0ff732 completed April 20, 2026, 5:36 p.m.
Created at: April 8, 2026, 9:37 p.m.