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.