Triple

T347173
Position Surface form Disambiguated ID Type / Status
Subject Nathanael Greene E6966 entity
Predicate familyName P18 FINISHED
Object Greene
Greene is a common English surname borne by numerous notable figures in politics, the military, the arts, and other fields.
 E43976 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: Greene | Statement: [Nathanael Greene, familyName, Greene]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Greene
Context triple: [Nathanael Greene, familyName, Greene]
  • A. Garner
    Garner is a surname most notably associated with John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
  • B. Richardson
    Richardson is a suburban city in the Dallas–Fort Worth metropolitan area known for its telecommunications industry and the University of Texas at Dallas.
  • C. Stevens
    Stevens is a common English-language surname borne by numerous notable individuals across fields such as sports, politics, arts, and academia.
  • D. Stearns
    Stearns is the middle name of the influential modernist poet and critic T. S. Eliot, whose full name is Thomas Stearns Eliot.
  • E. Graham
    Graham is the surname of Elizabeth Arden, the pioneering Canadian-American businesswoman who founded the iconic Elizabeth Arden cosmetics empire.
  • 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: Greene
Triple: [Nathanael Greene, familyName, Greene]
Generated description
Greene is a common English surname borne by numerous notable figures in politics, the military, the arts, and other fields.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Greene
Target entity description: Greene is a common English surname borne by numerous notable figures in politics, the military, the arts, and other fields.
  • A. Garner
    Garner is a surname most notably associated with John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
  • B. Richardson
    Richardson is a suburban city in the Dallas–Fort Worth metropolitan area known for its telecommunications industry and the University of Texas at Dallas.
  • C. Stevens
    Stevens is a common English-language surname borne by numerous notable individuals across fields such as sports, politics, arts, and academia.
  • D. Stearns
    Stearns is the middle name of the influential modernist poet and critic T. S. Eliot, whose full name is Thomas Stearns Eliot.
  • E. Graham
    Graham is the surname of Elizabeth Arden, the pioneering Canadian-American businesswoman who founded the iconic Elizabeth Arden cosmetics empire.
  • 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_69a2e7951ba08190960e90823b5078f3 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2eb1a37c08190b1380f6bf8513a37 completed Feb. 28, 2026, 1:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3d7eb6b708190b0dff991c101104f completed March 1, 2026, 6:08 a.m.
NEDg Description generation batch_69a3d85312448190aad22189ad07687a completed March 1, 2026, 6:10 a.m.
NED2 Entity disambiguation (via description) batch_69a3d8f2d0688190aa6d0a262f8857ba completed March 1, 2026, 6:13 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.