Triple

T5070067
Position Surface form Disambiguated ID Type / Status
Subject Fink E114254 entity
Predicate familyName P18 FINISHED
Object Greenall
Greenall is an English surname, historically associated with families involved in brewing and regional business in the United Kingdom.
 E490714 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: Greenall | Statement: [Fink, familyName, Greenall]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Greenall
Context triple: [Fink, familyName, Greenall]
  • A. Goose Green
    Goose Green is a settlement on East Falkland in the Falkland Islands, best known as the site of a major land battle during the 1982 Falklands War.
  • B. Goose Green
    Goose Green is a small public park and open green space in the East Dulwich area of south London, popular for recreation and community events.
  • C. Greenleaf
    Greenleaf is a dramatic television series that explores the secrets, scandals, and power struggles within a wealthy African-American megachurch family.
  • D. Greenleaf
    Greenleaf is the middle name of the 19th-century American Quaker poet and abolitionist John Greenleaf Whittier.
  • E. Greenleaf
    "Greenleaf" is a short story by Flannery O’Connor that explores themes of faith, violence, and grace through the tense relationship between a farm owner and her hired family.
  • 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: Greenall
Triple: [Fink, familyName, Greenall]
Generated description
Greenall is an English surname, historically associated with families involved in brewing and regional business in the United Kingdom.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Greenall
Target entity description: Greenall is an English surname, historically associated with families involved in brewing and regional business in the United Kingdom.
  • A. Goose Green
    Goose Green is a settlement on East Falkland in the Falkland Islands, best known as the site of a major land battle during the 1982 Falklands War.
  • B. Goose Green
    Goose Green is a small public park and open green space in the East Dulwich area of south London, popular for recreation and community events.
  • C. Greenleaf
    Greenleaf is the middle name of the 19th-century American Quaker poet and abolitionist John Greenleaf Whittier.
  • D. Greenleaf
    Greenleaf is a dramatic television series that explores the secrets, scandals, and power struggles within a wealthy African-American megachurch family.
  • E. Greenleaf
    "Greenleaf" is a short story by Flannery O’Connor that explores themes of faith, violence, and grace through the tense relationship between a farm owner and her hired family.
  • 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_69bd443cf28c8190ad371d603563dbdd completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd749f47908190891ac8432c5b5615 completed March 20, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69bea4a348e081909ccba9ce469c722c completed March 21, 2026, 2:01 p.m.
NEDg Description generation batch_69bea584a5a081908b6cf5abf1be393e completed March 21, 2026, 2:04 p.m.
NED2 Entity disambiguation (via description) batch_69bea61dbff08190819dc8da376d5e6d completed March 21, 2026, 2:07 p.m.
Created at: March 20, 2026, 1:39 p.m.