Triple
T5019363
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Carpenter films |
E112810
|
entity |
| Predicate | typicalVisualStyle |
P61564
|
FINISHED |
| Object | widescreen cinematography |
—
|
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: widescreen cinematography | Statement: [John Carpenter films, typicalVisualStyle, widescreen cinematography]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalVisualStyle Context triple: [John Carpenter films, typicalVisualStyle, widescreen cinematography]
-
A.
uniformStyle
Indicates that the related entities share the same or a consistent style, pattern, or formatting.
-
B.
traditionalStyle
Indicates that something follows or embodies a conventional, long-established way of doing, making, or presenting it, in contrast to modern or innovative styles.
-
C.
typicalColorDescription
Indicates the usual or characteristic color associated with an entity.
-
D.
structuralStyle
Indicates the architectural or design style that characterizes the structure or form of an entity.
-
E.
styleTendsTo
Indicates that one style is generally inclined or likely to develop, appear, or be adopted in the direction of another style.
- 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_69bd4435c2f48190be593158cbfcf8a3 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7342c62881909acb35849da8761c |
completed | March 20, 2026, 4:18 p.m. |
| PD | Predicate disambiguation | batch_69bd714ecfe08190b5830cfc1c74fa17 |
completed | March 20, 2026, 4:09 p.m. |
| PDg | Predicate description generation | batch_69bd72e1b7cc8190b2e621fdf8f22e38 |
completed | March 20, 2026, 4:16 p.m. |
Created at: March 20, 2026, 1:35 p.m.