Triple
T11994793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Ladies Man |
E285500
|
entity |
| Predicate | hasSlapstickStyle |
P14479
|
FINISHED |
| Object | Jerry Lewis slapstick comedy |
—
|
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: Jerry Lewis slapstick comedy | Statement: [The Ladies Man, hasSlapstickStyle, Jerry Lewis slapstick comedy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSlapstickStyle Context triple: [The Ladies Man, hasSlapstickStyle, Jerry Lewis slapstick comedy]
-
A.
hasComedyElements
Indicates that something contains humorous or comedic aspects as part of its overall content or style.
-
B.
hasDramaticStyle
Indicates that an entity employs or is characterized by a theatrical, emotionally intense, or striking manner of expression or presentation.
-
C.
hasHumorType
chosen
Indicates that an entity possesses or is characterized by a particular style, category, or type of humor.
-
D.
hasFilmStyle
Indicates that a film exhibits or is characterized by a particular cinematic style or aesthetic approach.
-
E.
isHumorousCharacter
Indicates that the character is portrayed in a humorous way or primarily serves a comedic role in the context.
- 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_69d6ab44a77c8190a652f4b27164e4ef |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903b211688190bfe6dd15c3f96d2f |
completed | April 10, 2026, 2:05 p.m. |
| PD | Predicate disambiguation | batch_69d902abca70819098291aa51b593708 |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:46 p.m.