Triple
T1690309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Surface Duo |
E36535
|
entity |
| Predicate | thicknessUnfolded |
P9690
|
FINISHED |
| Object | 4.8 mm per side |
—
|
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: 4.8 mm per side | Statement: [Surface Duo, thicknessUnfolded, 4.8 mm per side]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: thicknessUnfolded Context triple: [Surface Duo, thicknessUnfolded, 4.8 mm per side]
-
A.
thickness
chosen
Indicates the measure of how deep or wide an object or layer is from one surface or side to its opposite.
-
B.
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.
-
C.
depthMetresApprox
Indicates an approximate measurement of how deep something is in metres, rather than an exact value.
-
D.
deckArmorThickness
Indicates the thickness of the armor plating on the horizontal deck surface of a vehicle, vessel, or structure.
-
E.
lengthVariesBy
Indicates that the length of one entity changes or differs depending on another specified factor or condition.
- 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_69a886151508819084fa7f1ce6e05577 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaf3359ce48190803b322db8ad6027 |
completed | March 6, 2026, 3:31 p.m. |
| PD | Predicate disambiguation | batch_69aa61b71cec8190b273588051058ebd |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:29 p.m.