Triple

T16369278
Position Surface form Disambiguated ID Type / Status
Subject Christian Smith E397520 entity
Predicate hasSurname P18 FINISHED
Object Smith
Smith is a common English-language surname borne by numerous notable individuals across diverse fields such as politics, sports, science, and the arts.
E30542 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: Smith | Statement: [Christian Smith, hasSurname, Smith]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Smith
Context triple: [Christian Smith, hasSurname, Smith]
  • A. John
    John is the given name of John Albert William Spencer-Churchill, a British aristocrat and 10th Duke of Marlborough.
  • 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 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."
  • D. John
    John is the given name of John Bowen, a British novelist and playwright known for his crime and speculative fiction.
  • E. John
    John is the given name of John Boyle O'Reilly, a 19th-century Irish-born poet, journalist, and civil rights activist who became influential in the United States.
  • 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: Smith
Triple: [Christian Smith, hasSurname, Smith]
Generated description
Smith is a common English-language surname borne by numerous notable individuals across diverse fields such as politics, sports, science, and the arts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Smith
Target entity description: Smith is a common English-language surname borne by numerous notable individuals across diverse fields such as politics, sports, science, and the arts.
  • A. Smith chosen
    Smith is a common English surname borne by numerous notable individuals across diverse fields such as politics, arts, sports, and academia.
  • B. Jones
    Jones is a common English-language surname borne by numerous notable individuals across fields such as entertainment, sports, politics, and science.
  • C. John
    John I, Count of Holland, was a medieval nobleman who ruled the County of Holland at the turn of the 14th century.
  • 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.

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_69d87f2778dc8190aa95c7572db127e6 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2ff4021e88190ad093bab74cf82a4 completed April 18, 2026, 3:49 a.m.
NED1 Entity disambiguation (via context triple) batch_6a002dc29f088190ba5d69ff3c12a251 completed May 10, 2026, 7:03 a.m.
NEDg Description generation batch_6a002ec2fd948190878af958d0b90ce6 completed May 10, 2026, 7:07 a.m.
NED2 Entity disambiguation (via description) batch_6a00312a4fc48190b6bd6ad9db71bb4d completed May 10, 2026, 7:18 a.m.
Created at: April 10, 2026, 5:08 a.m.