Triple
T32619881
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Speed-the-Plow |
E833891
|
entity |
| Predicate | hasFilmIndustrySatire |
P186118
|
FINISHED |
| Object | Hollywood studio deal-making |
—
|
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: Hollywood studio deal-making | Statement: [Speed-the-Plow, hasFilmIndustrySatire, Hollywood studio deal-making]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmIndustrySatire Context triple: [Speed-the-Plow, hasFilmIndustrySatire, Hollywood studio deal-making]
-
A.
filmGenreParodied
Indicates that one work humorously imitates, mocks, or satirizes the conventions and characteristics of a particular film genre.
-
B.
filmIndustryDepicted
chosen
Indicates that the work portrays or represents aspects of the film industry within its content or narrative.
-
C.
sangForFilmIndustry
Indicates that a person performed singing specifically for use in the film industry, such as in movies or film soundtracks.
-
D.
hasFilmCareer
Indicates that an entity has been professionally involved in the film industry as a career.
-
E.
hasNotableSatire
Indicates that one entity is recognized for containing or exemplifying a significant satirical treatment of 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_69f3492ccc80819086ef7d26e9786647 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fee691952c8190822da83e46311d1d |
completed | May 9, 2026, 7:47 a.m. |
| PD | Predicate disambiguation | batch_69fee62f285c8190a625562a9b80526e |
completed | May 9, 2026, 7:45 a.m. |
Created at: May 1, 2026, 1:06 a.m.