Triple

T9746496
Position Surface form Disambiguated ID Type / Status
Subject Queen Mary E236322 entity
Predicate givenName P17 FINISHED
Object Victoria
Victoria is the given name of Queen Mary of the United Kingdom, reflecting her connection to the British royal family.
E134198 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: Victoria | Statement: [Queen Mary, givenName, Victoria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Victoria
Context triple: [Queen Mary, givenName, Victoria]
  • A. Victoria
    Victoria was the Spanish carrack that became the first ship to successfully circumnavigate the globe during Ferdinand Magellan’s expedition.
  • B. Victoria
    Victoria is a central London district known for its major transport hub, theatres, offices, and proximity to landmarks like Buckingham Palace.
  • C. Victoria
    Victoria is a southeastern Australian state known for its capital city Melbourne, cultural diversity, and varied landscapes ranging from coastal regions to alpine areas.
  • D. Victoria
    Victoria is a coastal municipality in the province of Northern Samar in the Philippines, known for its rural communities and agricultural economy.
  • E. Victoria
    Victoria is a popular Mexican lager beer brand owned by global brewing company AB InBev.
  • 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: Victoria
Triple: [Queen Mary, givenName, Victoria]
Generated description
Victoria is the given name of Queen Mary of the United Kingdom, reflecting her connection to the British royal family.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Victoria
Target entity description: Victoria is the given name of Queen Mary of the United Kingdom, reflecting her connection to the British royal family.
  • A. Victoria chosen
    Victoria is a feminine given name of Latin origin meaning "victory," borne by numerous notable figures including queens, saints, and public personalities.
  • B. Victoria
    Victoria was the long-reigning 19th-century British queen whose era saw vast industrial, cultural, and imperial expansion.
  • C. Victoria
    Victoria was a German princess of Saxe-Coburg-Saalfeld best known as the mother of Queen Victoria of the United Kingdom.
  • D. Victoria
    Victoria was the eldest child of Queen Victoria and Prince Albert, who became German Empress and Queen of Prussia through her marriage to Frederick III.
  • E. Victoria
    Victoria is a British historical drama television series that chronicles the early life and reign of Queen Victoria.
  • F. None of above.

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_69ca84d3e24481908a476e2231123cf9 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9f65ad788190b68d731b6f516d93 completed April 1, 2026, 10:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1afb089e48190a14ed0c0f81872c7 completed April 5, 2026, 12:41 a.m.
NEDg Description generation batch_69d1b06d39b48190adaadbc81b4ffb9a completed April 5, 2026, 12:44 a.m.
NED2 Entity disambiguation (via description) batch_69d1b1511c7c8190ba7bc691ab2d3a13 completed April 5, 2026, 12:48 a.m.
Created at: March 30, 2026, 8:23 p.m.