Triple

T15619012
Position Surface form Disambiguated ID Type / Status
Subject George Bruns E375495 entity
Predicate givenName P17 FINISHED
Object George
George is a masculine given name of Greek origin meaning "farmer" or "earthworker," widely used in many English-speaking and European countries.
 E372348 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 Bruns, givenName, George]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: George
Context triple: [George Bruns, 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 Bruns, givenName, George]
Generated description
George is a masculine given name of Greek origin meaning "farmer" or "earthworker," widely used in many English-speaking and European countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: George
Target entity description: George is a masculine given name of Greek origin meaning "farmer" or "earthworker," widely used in many English-speaking and European countries.
  • A. George chosen
    George is a masculine given name of Greek origin meaning "farmer" or "earthworker," widely used in English-speaking countries and beyond.
  • B. George
    George is a masculine given name of Greek origin meaning "farmer" or "earthworker," widely used in English-speaking and many other cultures.
  • C. George
    George is a common masculine given name of Greek origin, meaning "farmer" or "earthworker."
  • D. George
    George is a masculine given name of Greek origin, commonly used in English-speaking countries and borne by numerous historical and contemporary figures.
  • E. George
    George is a male given name commonly used in English-speaking countries and borne by numerous historical figures, including kings, presidents, and cultural icons.
  • 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_69d85ccf2794819096cda4cbcb02d478 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04e997ce481909b2f10d25705fbc6 completed April 16, 2026, 2:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff5f37383c81909d0efce84508a034 completed May 9, 2026, 4:22 p.m.
NEDg Description generation batch_69ff608edeec8190a7f847c682772cd4 completed May 9, 2026, 4:27 p.m.
NED2 Entity disambiguation (via description) batch_69ff6136a8c88190a83ad9232a338082 completed May 9, 2026, 4:30 p.m.
Created at: April 10, 2026, 4:13 a.m.