Triple

T6679776
Position Surface form Disambiguated ID Type / Status
Subject George C. Pimentel E151947 entity
Predicate givenName P17 FINISHED
Object George
George is the given name of George C. Pimentel, a prominent American chemist known for his work in chemical lasers and molecular spectroscopy.
E611664 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 C. Pimentel, givenName, George]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: George
Context triple: [George C. Pimentel, givenName, George]
  • A. George
    George is the first name of George Washington, the first President of the United States and a key leader in the American Revolutionary War.
  • B. George
    George is the given name of George Brydges Rodney, an 18th-century British naval officer and admiral noted for his victories during the American Revolutionary War.
  • C. George
    George is the given name of George Armstrong Custer, the controversial U.S. Army officer and cavalry commander best known for his defeat and death at the Battle of the Little Bighorn.
  • D. 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.
  • E. George
    George is the given name of George Monck, 1st Duke of Albemarle, a key English soldier and statesman who helped restore Charles II to the throne in 1660.
  • 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 C. Pimentel, givenName, George]
Generated description
George is the given name of George C. Pimentel, a prominent American chemist known for his work in chemical lasers and molecular spectroscopy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: George
Target entity description: George is the given name of George C. Pimentel, a prominent American chemist known for his work in chemical lasers and molecular spectroscopy.
  • A. George
    George is the given name of George Ellery Hale, the influential American solar astronomer and founder of several major observatories.
  • B. George
    George is the given name of George Bellas Greenough, a pioneering 19th-century English geologist and founding figure of the Geological Society of London.
  • C. George
    George is the given name of George Patton IV, a U.S. Army general and son of the famed World War II General George S. Patton.
  • D. 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.
  • E. George
    George is the given name of George K. Zipf, the American linguist and philologist known for formulating Zipf's law about word frequency distributions.
  • 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_69c687f830bc81909eb8b04dbb8450b1 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6b11df8d88190bf19fcb4e7a0bdb3 completed March 27, 2026, 4:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6f79f1718819098d8a6d08bf7f919 completed March 27, 2026, 9:33 p.m.
NEDg Description generation batch_69c6f8b1e1f48190bc9058a8a21a4a62 completed March 27, 2026, 9:37 p.m.
NED2 Entity disambiguation (via description) batch_69c6f9441d74819098f0639a29fdeb5e completed March 27, 2026, 9:40 p.m.
Created at: March 27, 2026, 2:03 p.m.