Triple
T27104721
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big Business |
E686534
|
entity |
| Predicate | hasSlapstickSetPiece |
P37148
|
FINISHED |
| Object | progressive destruction of house and car |
—
|
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: progressive destruction of house and car | Statement: [Big Business, hasSlapstickSetPiece, progressive destruction of house and car]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSlapstickSetPiece Context triple: [Big Business, hasSlapstickSetPiece, progressive destruction of house and car]
-
A.
hasSetPiece
chosen
Indicates that an event, scene, or work includes a distinct, often elaborate set piece as a notable component.
-
B.
hasComedyElements
Indicates that something contains humorous or comedic aspects as part of its overall content or style.
-
C.
hasStunts
Indicates that one entity performs, includes, or is associated with stunt actions for another entity or context.
-
D.
usesInComedy
Indicates that something is employed or incorporated as a humorous element within a comedic context or performance.
-
E.
hasStuntDouble
Indicates that one entity serves as a stunt double who performs dangerous or physically demanding actions on behalf of another 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_69ef148accd48190b6ed6e13a15f2a4f |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f623fcc6c881908e76b65c0ee51dd4 |
completed | May 2, 2026, 4:19 p.m. |
| PD | Predicate disambiguation | batch_69f61b40f02081909bd9c3ea73249163 |
completed | May 2, 2026, 3:41 p.m. |
Created at: April 27, 2026, 8:50 a.m.