Triple
T29389872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MultiMediaCard |
E745345
|
entity |
| Predicate | physicalThickness |
P9690
|
FINISHED |
| Object | 1.4 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: 1.4 mm | Statement: [MultiMediaCard, physicalThickness, 1.4 mm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: physicalThickness Context triple: [MultiMediaCard, physicalThickness, 1.4 mm]
-
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.
hasThicknessRange
Indicates that an entity is associated with a minimum and maximum thickness value defining the range of its thickness.
-
C.
physicalHeight
Indicates the vertical size or stature of an entity, typically measured as the distance from its base to its highest point.
-
D.
wallThicknessComparedTo
Indicates how the thickness of one wall relates to the thickness of another wall, typically in terms of being greater, equal, or less.
-
E.
bodyThickness
Indicates the measured or relative thickness of an entity’s body in the context of a comparison or description.
- 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_69f0a79dfabc81908755382ee47791e2 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69f669d578608190962d35116a01420c |
completed | May 2, 2026, 9:17 p.m. |
| PD | Predicate disambiguation | batch_69f6659b62fc8190b21555d0ba54db2d |
completed | May 2, 2026, 8:59 p.m. |
Created at: April 28, 2026, 2:41 p.m.