Triple
T160582
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Campbell's Soup Cans |
E3275
|
entity |
| Predicate | eachCanvasDimensions |
P3989
|
FINISHED |
| Object | 20 inches × 16 inches |
—
|
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: 20 inches × 16 inches | Statement: [Campbell's Soup Cans, eachCanvasDimensions, 20 inches × 16 inches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eachCanvasDimensions Context triple: [Campbell's Soup Cans, eachCanvasDimensions, 20 inches × 16 inches]
-
A.
hasDimension
Indicates that an entity possesses a specific measurable extent or size along one or more axes (e.g., length, width, height).
-
B.
typicalHeight
Indicates the usual or characteristic height associated with an entity, such as a person, object, or species.
-
C.
hasWidth
chosen
Indicates that an entity possesses a specific measurement or extent along its width dimension.
-
D.
typicalUnitSize
Indicates the standard or most common size or quantity in which something is typically measured, packaged, or used.
-
E.
paintedEvery
Indicates that an entity applied paint to each and every relevant item in a specified set or domain.
- 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_69a2527757ec819090b8becb2cf1a862 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a25856d934819095460b2ea566eb6b |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a256623704819089d9eeefe05858ce |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.