Triple

T12687735
Position Surface form Disambiguated ID Type / Status
Subject Lady Helen Taylor E303118 entity
Predicate marriedName P18 FINISHED
Object Taylor
Taylor is the married surname of Lady Helen Taylor, a member of the British royal family and daughter of Prince Edward, Duke of Kent.
E63210 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: Taylor | Statement: [Lady Helen Taylor, marriedName, Taylor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taylor
Context triple: [Lady Helen Taylor, marriedName, Taylor]
  • A. John
    John is the birth name of American singer-songwriter and producer Teddy Geiger, known for writing and producing hits for artists like Shawn Mendes.
  • B. John
    John I, Count of Holland, was a medieval nobleman who ruled the County of Holland at the turn of the 14th century.
  • C. John
    John McDowell is a prominent South African-born philosopher known for his influential work in epistemology, philosophy of mind, and ethics.
  • D. John
    John Cicero was a late 15th-century Elector of Brandenburg from the House of Hohenzollern who helped consolidate the territory’s political and administrative structures within the Holy Roman Empire.
  • E. John
    John Brabourne was a British film and television producer and peer, known for producing works such as the 1979 adaptation of "Murder on the Orient Express."
  • 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: Taylor
Triple: [Lady Helen Taylor, marriedName, Taylor]
Generated description
Taylor is the married surname of Lady Helen Taylor, a member of the British royal family and daughter of Prince Edward, Duke of Kent.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Taylor
Target entity description: Taylor is the married surname of Lady Helen Taylor, a member of the British royal family and daughter of Prince Edward, Duke of Kent.
  • A. Taylor chosen
    Taylor is a common English surname borne by numerous notable individuals across fields such as politics, arts, sports, and academia.
  • B. Taylor
    Taylor is a suburban city in Wayne County, Michigan, known for its residential communities and proximity to Detroit.
  • C. Tyler
    Tyler is the officer in a Masonic lodge responsible for guarding the entrance and ensuring only qualified individuals are admitted to meetings.
  • D. Tyler
    Tyler is a fictional character appearing in the American television series "Kristin."
  • E. Tyler
    Tyler is a masculine given name commonly used in English-speaking countries, originally derived from an occupational surname meaning "tile maker" or "house builder."
  • 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_69d7bdee64a08190801c6d470aefd723 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d961d8cb048190864a85dd75648820 completed April 10, 2026, 8:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69f671aacfa8819088fd113474638238 completed May 2, 2026, 9:50 p.m.
NEDg Description generation batch_69f6751f79148190a1735abf489fd106 completed May 2, 2026, 10:05 p.m.
NED2 Entity disambiguation (via description) batch_69f675c42fec8190b60751c0db88f3b6 completed May 2, 2026, 10:08 p.m.
Created at: April 9, 2026, 5:21 p.m.