Triple

T13944874
Position Surface form Disambiguated ID Type / Status
Subject George Bancroft E335354 entity
Predicate givenName P17 FINISHED
Object George
George is a common masculine given name of Greek origin, meaning "farmer" or "earthworker."
 E372351 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 Bancroft, givenName, George]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: George
Context triple: [George Bancroft, givenName, George]
  • A. George
    George is the heroic protagonist of the fantasy film "The Magic Sword," known for embarking on a perilous quest to rescue a princess from an evil sorcerer.
  • B. George
    George is a common English surname of likely Greek and Latin origin, associated with numerous notable historical and contemporary figures.
  • C. George
    George is the given name of George de Hevesy, the Hungarian radiochemist and Nobel laureate known for pioneering the use of radioactive tracers in studying chemical processes.
  • D. George
    George is the given name of George Habash, the Palestinian Christian physician and founder of the Popular Front for the Liberation of Palestine.
  • E. George
    George is a supporting character in the romantic comedy film "27 Dresses," serving as a colleague and love interest within the story’s central wedding-planning world.
  • 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 Bancroft, givenName, George]
Generated description
George is a common masculine given name of Greek origin, meaning "farmer" or "earthworker."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: George
Target entity description: George is a common masculine given name of Greek origin, meaning "farmer" or "earthworker."
  • A. George chosen
    George is a common masculine given name of Greek origin, meaning "farmer" or "earthworker."
  • 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 masculine given name of Greek origin meaning "farmer" or "earthworker," widely used in English-speaking countries and beyond.
  • 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_69d81c6081b88190b53e317c3370c8fe completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2e10f60c81908ee9636e85c070ff completed April 14, 2026, 12:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7ce7419cc81909488871c16d6b356 completed May 3, 2026, 10:38 p.m.
NEDg Description generation batch_69f7cf0462688190a6fea6afc9f38c7c completed May 3, 2026, 10:41 p.m.
NED2 Entity disambiguation (via description) batch_69f7cfa34a448190affb5b86efc37cf4 completed May 3, 2026, 10:43 p.m.
Created at: April 9, 2026, 10:17 p.m.