Triple

T8959664
Position Surface form Disambiguated ID Type / Status
Subject F. Albert Cotton E213568 entity
Predicate givenName P17 FINISHED
Object Frank
Frank is the given name of F. Albert Cotton, a prominent American chemist known for his work in inorganic chemistry and metal–metal bonding.
E769560 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: Frank | Statement: [F. Albert Cotton, givenName, Frank]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frank
Context triple: [F. Albert Cotton, givenName, Frank]
  • A. Frank
    Frank is a key supporting character in the post-apocalyptic horror film "28 Days Later," known as a protective father trying to keep his daughter safe amid a devastating viral outbreak in London.
  • B. Frank
    Frank is the given name of Frank Abagnale Jr., the infamous former con artist whose life inspired the film "Catch Me If You Can."
  • C. Frank
    Frank is the given name of British screenwriter and children's author Frank Cottrell-Boyce.
  • D. Frank
    Frank is the given name of British former professional heavyweight boxer Frank Bruno, a popular sports figure especially known in the UK.
  • E. Frank
    Frank is an alternate given name of longtime Republican U.S. Congressman Jim Sensenbrenner, who represented a Wisconsin district in the House of Representatives for four decades.
  • 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: Frank
Triple: [F. Albert Cotton, givenName, Frank]
Generated description
Frank is the given name of F. Albert Cotton, a prominent American chemist known for his work in inorganic chemistry and metal–metal bonding.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Frank
Target entity description: Frank is the given name of F. Albert Cotton, a prominent American chemist known for his work in inorganic chemistry and metal–metal bonding.
  • A. Frank
    Frank is the given name of Frank H. Westheimer, a prominent American chemist known for his influential work in physical organic chemistry.
  • B. Frank
    Frank is the given name of Frank Lampard, the renowned English former professional footballer and manager.
  • C. Frank
    Frank is the given name of the British philosopher, mathematician, and economist F. P. Ramsey, known for his influential work in logic, probability, and the foundations of mathematics.
  • D. Frank
    Frank is the given name of Frank Oz, the renowned puppeteer, actor, and director best known for his work with the Muppets and on Star Wars.
  • E. Frank
    Frank is the given name of the American painter, sculptor, and printmaker Frank Stella, a leading figure in minimalism and post-painterly abstraction.
  • 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_69ca8399ad2081909f8fa41d4314c215 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6746fbf88190aba658b4b9c2e4b0 completed April 1, 2026, 12:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc946a2f88190b7cd0fca67d31dfd completed April 3, 2026, 2:05 p.m.
NEDg Description generation batch_69cfca41aed08190a5107597625e4b61 completed April 3, 2026, 2:10 p.m.
NED2 Entity disambiguation (via description) batch_69cfcaba165081908c7bbfb905356942 completed April 3, 2026, 2:12 p.m.
Created at: March 30, 2026, 7 p.m.