Triple
T26034239
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | curie |
E647509
|
entity |
| Predicate | submultiple |
P26378
|
FINISHED |
| Object | microcurie |
—
|
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: microcurie | Statement: [curie, submultiple, microcurie]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: submultiple Context triple: [curie, submultiple, microcurie]
-
A.
typicalSubmultiples
Indicates that one quantity represents a standard or commonly used fractional multiple of another quantity (e.g., milli-, micro-, kilo- as typical submultiples).
-
B.
minorUnitSubdivisions
Indicates that one administrative or organizational unit is subdivided into smaller, subordinate units.
-
C.
subMode
Indicates a more specific or subordinate mode that falls under or refines a broader, parent mode within a hierarchy of modes.
-
D.
minorSector
Indicates that one sector is a subordinate or less significant subdivision within a larger, primary sector.
-
E.
submultipleRelation
chosen
Indicates that one quantity is an exact submultiple of another, meaning it divides the other quantity into an integer number of equal parts.
- 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_69e77e8b60e88190a3b26c4f0032a2c2 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f6061bfb248190af47ba49de93abfc |
completed | May 2, 2026, 2:11 p.m. |
| PD | Predicate disambiguation | batch_69f5f7fba5248190945acf1561280799 |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 22, 2026, 9:06 a.m.