Triple

T12897704
Position Surface form Disambiguated ID Type / Status
Subject John of Luxembourg E308537 entity
Predicate givenName P17 FINISHED
Object John
John of Luxembourg was a 14th-century King of Bohemia and Count of Luxembourg, known for his chivalric reputation and death at the Battle of Crécy.
E350248 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 of Luxembourg, givenName, John]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John
Context triple: [John of Luxembourg, givenName, John]
  • A. John
    John is the given name of John J. Pershing, the famed American general who led the American Expeditionary Forces in World War I.
  • B. John
    John is the given first name of Johnny Kilbane, an American featherweight boxing champion from the early 20th century.
  • C. John
    John is the given name of John Albert William Spencer-Churchill, a British aristocrat and 10th Duke of Marlborough.
  • D. John
    John is the given first name of the 19th-century English theologian and social reformer Frederick Denison Maurice.
  • E. John
    John is the given name of American screenwriter John Michael Hayes, known for his work on several Alfred Hitchcock films.
  • 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 of Luxembourg, givenName, John]
Generated description
John of Luxembourg was a 14th-century King of Bohemia and Count of Luxembourg, known for his chivalric reputation and death at the Battle of Crécy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: John
Target entity description: John of Luxembourg was a 14th-century King of Bohemia and Count of Luxembourg, known for his chivalric reputation and death at the Battle of Crécy.
  • A. John chosen
    John of Bohemia was a 14th-century King of Bohemia and Count of Luxembourg, renowned as a chivalric warrior who famously died fighting blind at the Battle of Crécy.
  • B. John
    John II of France was a 14th-century King of France, known as "John the Good," whose reign was marked by the Hundred Years' War and his capture at the Battle of Poitiers.
  • C. John
    John, Prince of Lancaster, was a 15th-century English royal and military commander, best known for his role in the Hundred Years' War and as the younger son of King Henry IV.
  • D. John
    John the Fearless was a powerful early 15th-century Duke of Burgundy known for his aggressive political maneuvers and pivotal role in the French civil conflicts of the Hundred Years’ War.
  • E. John
    John V, Duke of Brittany, was a 15th-century French nobleman who ruled the Duchy of Brittany and played a significant role in the politics of the Hundred Years' War.
  • F. None of above.

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_69d7bdf7c1f0819098102569a8d8cbf5 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9717f3fc48190b61c8f6f36cd0725 completed April 10, 2026, 9:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6b8cec96c819089d253162bc4705a completed May 3, 2026, 2:54 a.m.
NEDg Description generation batch_69f6b9a1ca54819085da2ca592bf5219 completed May 3, 2026, 2:57 a.m.
NED2 Entity disambiguation (via description) batch_69f6bb2602848190b9588134c71d0ef4 completed May 3, 2026, 3:04 a.m.
Created at: April 9, 2026, 5:40 p.m.