Triple
T30357377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huawei P50 Pocket |
E772184
|
entity |
| Predicate | thicknessFolded |
P9690
|
FINISHED |
| Object | 15.2 mm |
—
|
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: 15.2 mm | Statement: [Huawei P50 Pocket, thicknessFolded, 15.2 mm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: thicknessFolded Context triple: [Huawei P50 Pocket, thicknessFolded, 15.2 mm]
-
A.
folded
Indicates that an entity has been bent or doubled over onto itself, typically along a line or crease, changing its original flat or extended form.
-
B.
thickness
chosen
Indicates the measure of how deep or wide an object or layer is from one surface or side to its opposite.
-
C.
folding
Indicates that one entity bends or doubles another entity (or itself) so that parts of it lie flat against or over each other.
-
D.
foldedYear
Indicates that one entity represents a year that has been combined, aggregated, or "folded" into another temporal grouping or representation.
-
E.
numberOfFolds
Indicates the count of times something has been folded or the total number of folds applied to an object or structure.
- 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_69f2248c6f5c8190a6177842bf791a3c |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6823fe8c48190a8911627f79dd949 |
completed | May 2, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69f678d019fc8190913662cd2f87b857 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 29, 2026, 7:57 p.m.