Triple

T4175700
Position Surface form Disambiguated ID Type / Status
Subject Parkinson Building E86469 entity
Predicate namedAfter P63 FINISHED
Object Frank Parkinson
Frank Parkinson was a prominent British industrialist and philanthropist whose contributions to education led to major university buildings being named in his honor.
E438914 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: Frank Parkinson | Statement: [Parkinson Building, namedAfter, Frank Parkinson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frank Parkinson
Context triple: [Parkinson Building, namedAfter, Frank Parkinson]
  • A. Ralph Brownrigg
    Ralph Brownrigg was a 17th-century English clergyman and academic who served as Bishop of Exeter in the Church of England.
  • B. George Ward
    George Ward was a British Conservative politician who served in senior government roles during the mid-20th century, including as Minister of Supply.
  • C. George Nichols
    George Nichols was a 19th-century American publisher known for issuing notable literary works, including influential satirical and poetic writings.
  • D. George Nichols
    George Nichols was an American actor and film director active during the silent film era.
  • E. George Lynn
    George Lynn was an American character actor active in mid-20th-century film and television, often appearing in crime dramas and genre pictures.
  • 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: Frank Parkinson
Triple: [Parkinson Building, namedAfter, Frank Parkinson]
Generated description
Frank Parkinson was a prominent British industrialist and philanthropist whose contributions to education led to major university buildings being named in his honor.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Frank Parkinson
Target entity description: Frank Parkinson was a prominent British industrialist and philanthropist whose contributions to education led to major university buildings being named in his honor.
  • A. Ralph Brownrigg
    Ralph Brownrigg was a 17th-century English clergyman and academic who served as Bishop of Exeter in the Church of England.
  • B. George Ward
    George Ward was a British Conservative politician who served in senior government roles during the mid-20th century, including as Minister of Supply.
  • C. George Nichols
    George Nichols was a 19th-century American publisher known for issuing notable literary works, including influential satirical and poetic writings.
  • D. George Nichols
    George Nichols was an American actor and film director active during the silent film era.
  • E. George Lynn
    George Lynn was an American character actor active in mid-20th-century film and television, often appearing in crime dramas and genre pictures.
  • 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_69aed93de98c8190ad838ce507b77c8a completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af02e9370481908eda048724261c2b completed March 9, 2026, 5:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69b6133e0b188190b459e1293d5cc2dc completed March 15, 2026, 2:02 a.m.
NEDg Description generation batch_69b61464b0dc81909cab007115435b8b completed March 15, 2026, 2:07 a.m.
NED2 Entity disambiguation (via description) batch_69b6151440648190bf8c1c95e20caf13 completed March 15, 2026, 2:10 a.m.
Created at: March 9, 2026, 3:45 p.m.