Triple

T14484799
Position Surface form Disambiguated ID Type / Status
Subject Prince Albert of Prussia E359198 entity
Predicate givenName P17 FINISHED
Object Albert
Albert was the given name of Prince Albert of Prussia, a 19th-century Prussian royal and military figure from the House of Hohenzollern.
E1101047 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: Albert | Statement: [Prince Albert of Prussia, givenName, Albert]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Albert
Context triple: [Prince Albert of Prussia, givenName, Albert]
  • A. Albert
    Albert was the given name of Albert I of Habsburg, a medieval King of Germany and member of the influential Habsburg dynasty.
  • B. Albert
    Albert is the given first name of Al Jackson Jr., the influential American drummer best known for his work with Booker T. & the M.G.'s and Stax Records.
  • C. Albert
    Albert is the first name of American television personality and longtime weather anchor Al Roker.
  • D. Albert
    Albert is the given name of the renowned theoretical physicist Albert Einstein, whose work revolutionized modern physics.
  • E. Albert
    Albert is the given name of Albert A. Michelson, the pioneering physicist known for his precise measurements of the speed of light and the Michelson–Morley experiment.
  • 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: Albert
Triple: [Prince Albert of Prussia, givenName, Albert]
Generated description
Albert was the given name of Prince Albert of Prussia, a 19th-century Prussian royal and military figure from the House of Hohenzollern.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Albert
Target entity description: Albert was the given name of Prince Albert of Prussia, a 19th-century Prussian royal and military figure from the House of Hohenzollern.
  • A. Albert
    Albert was the given name of Albert I of Habsburg, a medieval King of Germany and member of the influential Habsburg dynasty.
  • B. Albert
    Albert is the given name of the 19th-century German-American painter Albert Bierstadt, renowned for his grand landscapes of the American West.
  • C. Albert
    Albert is a surname of Germanic origin borne by various notable individuals across fields such as sports, politics, and the arts.
  • D. Albert
    Albert is a masculine given name of Germanic origin, commonly used in many European languages and English-speaking countries.
  • E. Albert
    Albert is the given name of Albert A. Michelson, the pioneering physicist known for his precise measurements of the speed of light and the Michelson–Morley experiment.
  • 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_69d8279740308190af9df93a3af8592e completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de924d7f4c8190b1f62b5ffe1ff649 completed April 14, 2026, 7:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd64a73cf48190811d6de182e891c4 completed May 8, 2026, 4:20 a.m.
NEDg Description generation batch_69fd66d3f2448190b8926c479ce95e83 completed May 8, 2026, 4:30 a.m.
NED2 Entity disambiguation (via description) batch_69fd67b5cd2c8190871d22ebdc64dd11 completed May 8, 2026, 4:33 a.m.
Created at: April 10, 2026, 1:20 a.m.