Triple
T19331630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York burlesque circuit |
E483509
|
entity |
| Predicate | typicalProgramComponent |
P66086
|
FINISHED |
| Object | comedy sketches |
—
|
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: comedy sketches | Statement: [New York burlesque circuit, typicalProgramComponent, comedy sketches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalProgramComponent Context triple: [New York burlesque circuit, typicalProgramComponent, comedy sketches]
-
A.
typicalProgramElement
Indicates that one program element is a representative or characteristic example of another program element or category of elements.
-
B.
programFeature
chosen
Indicates that a particular feature, capability, or component is part of, supported by, or provided within a given program.
-
C.
typicalComponent
Indicates that one entity is a standard or representative component or part of another entity.
-
D.
programType
Indicates the category or kind of program to which an entity belongs or with which it is associated.
-
E.
componentProgram
Indicates that one entity is a component or module that forms part of a larger program or software system represented by the other entity.
- F. None of above.
Provenance (3 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_69d8e8d13e3c81909d91d1d5ec37c095 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e616422fa08190bf4bde4312ec7cf3 |
completed | April 20, 2026, 12:04 p.m. |
| PD | Predicate disambiguation | batch_69e4dd12303c8190a2027c062b2dff40 |
completed | April 19, 2026, 1:48 p.m. |
Created at: April 10, 2026, 1:33 p.m.