Triple
T7188723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | sievert |
E167632
|
entity |
| Predicate | unitClass |
P71511
|
FINISHED |
| Object | dose equivalent unit |
—
|
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: dose equivalent unit | Statement: [sievert, unitClass, dose equivalent unit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: unitClass Context triple: [sievert, unitClass, dose equivalent unit]
-
A.
unit
Indicates that two quantities are measured using the same standard unit of measurement.
-
B.
unitSpecialization
chosen
Indicates that one unit is a specialized or more specific version of another unit within a hierarchical or categorical relationship.
-
C.
coreUnitType
Indicates that one entity is classified as the primary or fundamental type/category to which another entity (a core unit) belongs.
-
D.
unitRole
Indicates the functional role or purpose that a unit serves within a larger system or context.
-
E.
definesUnit
Indicates that one entity specifies the measurement unit or standard by which the value or quantity of another entity is expressed.
- 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_69c6888b5248819090499a884ee3ec39 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e8e3d9188190ba2792098d76fb86 |
completed | March 27, 2026, 8:30 p.m. |
| PD | Predicate disambiguation | batch_69c6e752385c819096fbab55566ee2a8 |
completed | March 27, 2026, 8:23 p.m. |
Created at: March 27, 2026, 2:50 p.m.