Triple

T6079413
Position Surface form Disambiguated ID Type / Status
Subject Parkinsonia E135483 entity
Predicate namedAfter P63 FINISHED
Object John Parkinson
John Parkinson was a notable English botanist and herbalist of the early 17th century, renowned for his influential plant catalogues and contributions to horticulture.
 E565972 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: John Parkinson | Statement: [Parkinsonia, namedAfter, John Parkinson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: John Parkinson
Context triple: [Parkinsonia, namedAfter, John Parkinson]
  • A. John Parkinson
    John Parkinson was a prominent early 20th-century architect known for shaping the skyline of Los Angeles with landmark civic and commercial buildings.
  • B. Robert Barker
    Robert Barker was a 17th-century English royal printer best known for printing the first edition of the King James Bible.
  • C. John Davies
    John Davies was a British Conservative politician and businessman who served in senior government roles during the late 1960s and early 1970s.
  • D. Thomas Hatfield
    Thomas Hatfield was a 14th-century Bishop of Durham and influential English cleric and statesman.
  • E. John Lyons
    John Lyons is a film producer best known for his work on major Hollywood comedies, including the Austin Powers series.
  • 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: John Parkinson
Triple: [Parkinsonia, namedAfter, John Parkinson]
Generated description
John Parkinson was a notable English botanist and herbalist of the early 17th century, renowned for his influential plant catalogues and contributions to horticulture.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: John Parkinson
Target entity description: John Parkinson was a notable English botanist and herbalist of the early 17th century, renowned for his influential plant catalogues and contributions to horticulture.
  • A. John Parkinson
    John Parkinson was a prominent early 20th-century architect known for shaping the skyline of Los Angeles with landmark civic and commercial buildings.
  • B. Robert Barker
    Robert Barker was a 17th-century English royal printer best known for printing the first edition of the King James Bible.
  • C. John Davies
    John Davies was a British Conservative politician and businessman who served in senior government roles during the late 1960s and early 1970s.
  • D. Thomas Hatfield
    Thomas Hatfield was a 14th-century Bishop of Durham and influential English cleric and statesman.
  • E. John Lyons
    John Lyons is a film producer best known for his work on major Hollywood comedies, including the Austin Powers series.
  • 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_69c0087ad31c8190ab936e0ff28614b6 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c0577209b88190afe5b1365cf6436d completed March 22, 2026, 8:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69c11d4dc7ec8190baeede11ac27e229 completed March 23, 2026, 11 a.m.
NEDg Description generation batch_69c11dc2becc8190991c444357755dec completed March 23, 2026, 11:02 a.m.
NED2 Entity disambiguation (via description) batch_69c11ed987e08190bf7065d04d9c3a0c completed March 23, 2026, 11:07 a.m.
Created at: March 22, 2026, 4:11 p.m.