Triple
T2522416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dolce & Gabbana The One |
E55552
|
entity |
| Predicate | bottleDesign |
P1529
|
FINISHED |
| Object | rectangular glass bottle |
—
|
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: rectangular glass bottle | Statement: [Dolce & Gabbana The One, bottleDesign, rectangular glass bottle]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bottleDesign Context triple: [Dolce & Gabbana The One, bottleDesign, rectangular glass bottle]
-
A.
hasBottleColor
Indicates that an entity is associated with a bottle characterized by a specific color.
-
B.
designedIn
Indicates that something was created, planned, or conceived during a particular time period or at a specific location.
-
C.
hasDesign
chosen
Indicates that one entity possesses, embodies, or is characterized by a particular design associated with another entity.
-
D.
designedWith
Indicates that one entity was created, planned, or developed using another entity as a tool, method, or guiding basis in its design process.
-
E.
drinks
Indicates that one entity consumes a liquid substance, typically by ingesting it through the mouth.
- 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_69ab49e4749c8190813311efd1630f1b |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd23895348190bb4dad6d7174893a |
completed | March 7, 2026, 7:22 a.m. |
| PD | Predicate disambiguation | batch_69abd0c144b0819092f32a13c1d127e5 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:46 p.m.