Triple
T31902599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Main Street Vehicles (Magic Kingdom) |
E814463
|
entity |
| Predicate | photographyUse |
P9792
|
FINISHED |
| Object | popular for guest photos |
—
|
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: popular for guest photos | Statement: [Main Street Vehicles (Magic Kingdom), photographyUse, popular for guest photos]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: photographyUse Context triple: [Main Street Vehicles (Magic Kingdom), photographyUse, popular for guest photos]
-
A.
usesPhotographyFrom
Indicates that one entity employs or incorporates photographic material originating from another entity.
-
B.
photographyGenre
Indicates the specific genre or style of photography that characterizes a photographic work or activity.
-
C.
usesImagery
Indicates that one entity employs descriptive or figurative language to create sensory or vivid mental images in relation to another entity or concept.
-
D.
isPhotographicSubject
chosen
Indicates that an entity serves as the subject or main focus captured in a photograph taken by another entity.
-
E.
allowsPhotography
Indicates that one entity permits another entity to take photographs in a particular context or location.
- 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_69f348f04d7881909537fc9e7cbc670e |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b21e7e088190832a3db585daea1c |
completed | May 3, 2026, 2:25 a.m. |
| PD | Predicate disambiguation | batch_69f6b14faf608190a25b977c0740729c |
completed | May 3, 2026, 2:22 a.m. |
Created at: May 1, 2026, midnight