Triple

T2955092
Position Surface form Disambiguated ID Type / Status
Subject Cooper E79912 entity
Predicate hasVariantForm P457 FINISHED
Object Couper
Couper is a surname and variant spelling of Cooper, used by various individuals and families, particularly in English-speaking regions.
 E315298 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: Couper | Statement: [Cooper, hasVariantForm, Couper]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Couper
Context triple: [Cooper, hasVariantForm, Couper]
  • A. Carr
    Carr is a common English and Irish surname with multiple notable bearers across fields such as sports, politics, and the arts.
  • B. Niva
    Niva was a prominent Russian literary and illustrated weekly magazine of the late 19th and early 20th centuries, known for publishing fiction, poetry, and cultural commentary.
  • C. Parker
    Parker is a common English surname borne by numerous notable individuals across fields such as politics, sports, arts, and science.
  • D. Carris
    Carris is the main public transport company in Lisbon, Portugal, operating the city's buses, trams, and certain historic lifts.
  • E. Sauer
    Sauer is a German surname borne by various notable individuals in fields such as science, politics, and the arts.
  • 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: Couper
Triple: [Cooper, hasVariantForm, Couper]
Generated description
Couper is a surname and variant spelling of Cooper, used by various individuals and families, particularly in English-speaking regions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Couper
Target entity description: Couper is a surname and variant spelling of Cooper, used by various individuals and families, particularly in English-speaking regions.
  • A. Carr
    Carr is a common English and Irish surname with multiple notable bearers across fields such as sports, politics, and the arts.
  • B. Niva
    Niva was a prominent Russian literary and illustrated weekly magazine of the late 19th and early 20th centuries, known for publishing fiction, poetry, and cultural commentary.
  • C. Parker
    Parker is a common English surname borne by numerous notable individuals across fields such as politics, sports, arts, and science.
  • D. Carris
    Carris is the main public transport company in Lisbon, Portugal, operating the city's buses, trams, and certain historic lifts.
  • E. Sauer
    Sauer is a German surname borne by various notable individuals in fields such as science, politics, and the arts.
  • 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_69ad8b1276588190a374a0b12e0f7bdf completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad99286ac8819084f02fbb0a1616d3 completed March 8, 2026, 3:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69b0fc86af78819089ff24f2621b151f completed March 11, 2026, 5:24 a.m.
NEDg Description generation batch_69b1003ac79c8190b25f12823b6010ed completed March 11, 2026, 5:40 a.m.
NED2 Entity disambiguation (via description) batch_69b10421ab508190a741b1973bd05b17 completed March 11, 2026, 5:56 a.m.
Created at: March 8, 2026, 2:57 p.m.