Triple

T18541396
Position Surface form Disambiguated ID Type / Status
Subject George II Rákóczi E453108 entity
Predicate givenName P17 FINISHED
Object George
George is the given name of George II Rákóczi, a 17th-century Prince of Transylvania from the influential Rákóczi noble family.
 E1329190 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: George | Statement: [George II Rákóczi, givenName, George]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: George
Context triple: [George II Rákóczi, givenName, George]
  • A. George
    George is the given first name of the fictional character Gob Bluth from the television series "Arrested Development."
  • B. George
    George is the middle name of William George Barker, a renowned Canadian World War I flying ace and Victoria Cross recipient.
  • C. George
    George is the given name of George Stanley, 9th Baron Strange, an English nobleman and politician of the late 15th century.
  • D. George
    George is the given name of George Carnegie, 6th Earl of Northesk, a Scottish nobleman and naval officer in the Royal Navy.
  • E. George
    George is the given name of Lord George Murray, a prominent Scottish Jacobite general during the 18th-century uprisings.
  • 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: George
Triple: [George II Rákóczi, givenName, George]
Generated description
George is the given name of George II Rákóczi, a 17th-century Prince of Transylvania from the influential Rákóczi noble family.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: George
Target entity description: George is the given name of George II Rákóczi, a 17th-century Prince of Transylvania from the influential Rákóczi noble family.
  • A. George
    George was the given name of George II, Landgrave of Hesse-Darmstadt, an 18th-century German nobleman and ruler within the Holy Roman Empire.
  • B. George
    George I Rákóczi was a 17th-century Prince of Transylvania known for strengthening the principality and navigating complex political relations with the Ottoman Empire and the Habsburgs.
  • C. George
    George is the given name of George Monck, a 17th-century English soldier and statesman instrumental in the Restoration of the monarchy under Charles II.
  • D. George
    George is the given name of George Villiers, 1st Earl of Clarendon, a prominent 17th-century English statesman and royal advisor.
  • E. George
    George is the given name of Lord George Murray, a prominent Scottish Jacobite general during the 18th-century uprisings.
  • 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_69d8d387b5548190aa030dad2cb4947e completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e534b749b88190ad03b26bee3c89df completed April 19, 2026, 8:01 p.m.
NED1 Entity disambiguation (via context triple) batch_6a049164ed7c81909af929b5969a5a8c completed May 13, 2026, 2:57 p.m.
NEDg Description generation batch_6a0492dfcde88190a05adf587a6a2a99 completed May 13, 2026, 3:03 p.m.
NED2 Entity disambiguation (via description) batch_6a04936cfd348190980a63cbea836b2e completed May 13, 2026, 3:06 p.m.
Created at: April 10, 2026, 11:37 a.m.