Triple

T10411804
Position Surface form Disambiguated ID Type / Status
Subject John, Abbot of Reading E245408 entity
Predicate givenName P17 FINISHED
Object John
John was an abbot of Reading Abbey, a senior monastic leader in the medieval English Benedictine community.
E863780 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 | Statement: [John, Abbot of Reading, givenName, John]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John
Context triple: [John, Abbot of Reading, givenName, John]
  • A. John
    John is the given name of John Key, the former Prime Minister of New Zealand and leader of the National Party.
  • B. John
    John is the middle name of American entrepreneur Henry John Heinz, founder of the H. J. Heinz Company known for its ketchup and other food products.
  • C. John
    John is the given name of Australian cinematographer John Seale, known for his work on films such as "The English Patient" and "Mad Max: Fury Road."
  • D. John
    John is the given name of John Dustin Archbold, an American oil industry executive and key figure in the early history of Standard Oil.
  • E. John
    John is the given name of John Brisker, an American professional basketball player known for his time in the ABA and NBA and his mysterious disappearance in the 1970s.
  • 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
Triple: [John, Abbot of Reading, givenName, John]
Generated description
John was an abbot of Reading Abbey, a senior monastic leader in the medieval English Benedictine community.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: John
Target entity description: John was an abbot of Reading Abbey, a senior monastic leader in the medieval English Benedictine community.
  • A. John
    John is the given name of Saint John of Capistrano, a 15th-century Franciscan friar renowned as a preacher, theologian, and leader in the defense of Belgrade.
  • B. John
    John is the given name of John Stott, a prominent 20th-century English Anglican priest, theologian, and influential evangelical leader.
  • C. John
    John was a medieval English monarch who ruled as King John of England from 1199 to 1216 and is best known for sealing the Magna Carta.
  • D. John
    John is the given name of Sir John Woodville, a 15th-century English nobleman associated with the influential Woodville family during the Wars of the Roses.
  • E. John
    John is the given name of J. C. Squire, a prominent British poet, literary critic, and editor of the early 20th century.
  • 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_69d381be340c8190b05998703d42d224 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e9fc72d081908d81c71133973daf completed April 7, 2026, 11:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69d87e9084fc81909e1d46a111a1ef2b completed April 10, 2026, 4:37 a.m.
NEDg Description generation batch_69d886c325c4819089dac35eb26e7961 completed April 10, 2026, 5:12 a.m.
NED2 Entity disambiguation (via description) batch_69d88dbbe97c8190861e08f3ff39f91b completed April 10, 2026, 5:42 a.m.
Created at: April 6, 2026, 12:10 p.m.