Triple
T36240270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | General Lee |
E891498
|
entity |
| Predicate | hasStuntUsage |
P45832
|
FINISHED |
| Object | many cars destroyed during jumps and crashes |
—
|
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: many cars destroyed during jumps and crashes | Statement: [General Lee, hasStuntUsage, many cars destroyed during jumps and crashes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStuntUsage Context triple: [General Lee, hasStuntUsage, many cars destroyed during jumps and crashes]
-
A.
hasStunts
chosen
Indicates that one entity performs, includes, or is associated with stunt actions for another entity or context.
-
B.
hasMotorcycleStuntRiderProtagonist
Indicates that the primary protagonist of the work is a motorcycle stunt rider.
-
C.
hasPublicityStunt
Indicates that an entity engages in or is associated with a planned publicity stunt intended to attract public attention.
-
D.
hasStuntDouble
Indicates that one entity serves as a stunt double who performs dangerous or physically demanding actions on behalf of another entity.
-
E.
featuresStuntShow
Indicates that something includes or presents a stunt show as part of its offerings or content.
- 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_69f76e44993481908fa75e4c48d0aab3 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a0039c2d5d48190b8ef2c7ef17d8dc5 |
completed | May 10, 2026, 7:54 a.m. |
| PD | Predicate disambiguation | batch_6a0038e525448190a4c815f51595e78d |
completed | May 10, 2026, 7:51 a.m. |
Created at: May 3, 2026, 4:09 p.m.