Triple
T30357683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CompactFlash |
E772191
|
entity |
| Predicate | thicknessOfTypeI |
P9690
|
FINISHED |
| Object | 3.3 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: 3.3 mm | Statement: [CompactFlash, thicknessOfTypeI, 3.3 mm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: thicknessOfTypeI Context triple: [CompactFlash, thicknessOfTypeI, 3.3 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.
wallThicknessComparedTo
Indicates how the thickness of one wall relates to the thickness of another wall, typically in terms of being greater, equal, or less.
-
D.
hasApproximateAverageThickness
Indicates that an entity possesses a thickness value that is an estimated or typical average rather than an exact measurement.
-
E.
thickestIn
Indicates that one entity has the greatest thickness among a specified set or within a given context.
- 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_69f67e40af9881908de3a4aa15f70a83 |
completed | May 2, 2026, 10:44 p.m. |
Created at: April 29, 2026, 7:57 p.m.