Triple
T35452667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zack Brown |
E1024678
|
entity |
| Predicate | filmProjectType |
P183305
|
FINISHED |
| Object | pornographic film |
—
|
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: pornographic film | Statement: [Zack Brown, filmProjectType, pornographic film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmProjectType Context triple: [Zack Brown, filmProjectType, pornographic film]
-
A.
filmProject
Indicates a relationship where an entity is involved in or associated with a specific film production project.
-
B.
filmProductionType
Indicates the specific kind or category of production under which a film was made (e.g., feature, short, documentary, TV movie).
-
C.
filmType
Indicates the specific category or genre that a film belongs to.
-
D.
filmTypeContext
Indicates the contextual relationship between a film and its type or category within a specific classification or usage setting.
-
E.
filmStockType
Indicates the specific type or category of photographic or motion picture film stock used or associated with an entity.
- F. None of above. chosen
Provenance (4 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_69f76df92f108190817222e520e22268 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79da9f80c8190b0afd8509f28747b |
completed | May 3, 2026, 7:10 p.m. |
| PD | Predicate disambiguation | batch_69f79617d40481909ba372f94209c08b |
completed | May 3, 2026, 6:38 p.m. |
| PDg | Predicate description generation | batch_69f79da8d8848190ab5ab1bdad95d58c |
completed | May 3, 2026, 7:10 p.m. |
Created at: May 3, 2026, 4:04 p.m.