Triple
T22166769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucky Star (1929 film) |
E547809
|
entity |
| Predicate | hasVisualEmphasis |
P17414
|
FINISHED |
| Object | atmospheric outdoor sets |
—
|
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: atmospheric outdoor sets | Statement: [Lucky Star (1929 film), hasVisualEmphasis, atmospheric outdoor sets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVisualEmphasis Context triple: [Lucky Star (1929 film), hasVisualEmphasis, atmospheric outdoor sets]
-
A.
hasEmphasis
chosen
Indicates that one element is given special stress, importance, or prominence relative to others.
-
B.
hasVisualCharacter
Indicates that one entity possesses or exhibits a particular visual appearance, style, or graphical characteristic defined by another entity.
-
C.
hasVisualImpact
Indicates that one entity affects or influences the visual appearance or aesthetic perception of another.
-
D.
hasVisuals
Indicates that one entity includes, displays, or is associated with visual elements or imagery related to another entity.
-
E.
hasExpressivity
Indicates that one entity possesses a certain level or type of expressive power, capability, or richness in representation relative to another.
- 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_69e11e3c4c5c81908d336165816b12e0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12a3213ec8190841439dbe470d545 |
completed | April 28, 2026, 9:44 p.m. |
| PD | Predicate disambiguation | batch_69e71b41555881909b8e22718974d527 |
completed | April 21, 2026, 6:37 a.m. |
Created at: April 16, 2026, 8:34 p.m.