Triple
T25831965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Burns and Allen vaudeville act |
E650686
|
entity |
| Predicate | roleOfGracieAllen |
P160690
|
FINISHED |
| Object | scatterbrained comic |
—
|
LITERAL FINISHED |
How this triple was built (2 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: scatterbrained comic | Statement: [Burns and Allen vaudeville act, roleOfGracieAllen, scatterbrained comic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleOfGracieAllen Context triple: [Burns and Allen vaudeville act, roleOfGracieAllen, scatterbrained comic]
-
A.
roleInTheMarvelousMrsMaisel
Indicates that an entity has a role or appearance in the television series "The Marvelous Mrs. Maisel."
-
B.
MarilynMonroeRoleType
Indicates the type or category of role associated with Marilyn Monroe in a given context.
-
C.
MaryAstorRole
Indicates that an entity represents a role or character portrayed by Mary Astor in a film, play, or other performance.
-
D.
performerRoleOfJerryLewis
Indicates that the specified role is one that was performed or played by Jerry Lewis.
-
E.
barbaraStanwyckRole
Indicates that the subject is a role or character portrayed by Barbara Stanwyck.
- F. None of above. chosen
Provenance (4 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_69e7ab37438081908f1ccf6284839520 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f60459a53c8190bbd90e80890eebcd |
completed | May 2, 2026, 2:04 p.m. |
| PD | Predicate disambiguation | batch_69f602d07590819085ac34b189613104 |
completed | May 2, 2026, 1:57 p.m. |
| PDg | Predicate description generation | batch_69f603b90c94819088d62cb9489e95ff |
completed | May 2, 2026, 2:01 p.m. |
Created at: April 22, 2026, 7:39 a.m.