Triple
T14165972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lonesome Ghosts |
E351074
|
entity |
| Predicate | usesSlapstickGags |
P45834
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Lonesome Ghosts, usesSlapstickGags, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesSlapstickGags Context triple: [Lonesome Ghosts, usesSlapstickGags, true]
-
A.
notableGag
Indicates that something features a particularly memorable or significant joke, comedic moment, or running gag.
-
B.
hasComedyElements
chosen
Indicates that something contains humorous or comedic aspects as part of its overall content or style.
-
C.
gimmick
Indicates that an entity uses or features a novel, attention-grabbing trick or device primarily intended to attract interest rather than provide substantive value.
-
D.
featuresMotherInLawGags
Indicates a relationship where content includes or focuses on jokes, pranks, or humorous situations involving a mother-in-law.
-
E.
humorSource
Indicates that one entity is the origin or cause of humor experienced in relation to 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_69d8278775fc8190b0802d22ca2f495d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61b207cc8190b85b1ff0910b54da |
completed | April 14, 2026, 3:48 p.m. |
| PD | Predicate disambiguation | batch_69de05b8434c81908c33b1b513463b12 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 1 a.m.