Triple
T31792937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doppler cooling |
E811517
|
entity |
| Predicate | typicalLaserIntensity |
P142438
|
FINISHED |
| Object | on the order of saturation intensity |
—
|
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: on the order of saturation intensity | Statement: [Doppler cooling, typicalLaserIntensity, on the order of saturation intensity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalLaserIntensity Context triple: [Doppler cooling, typicalLaserIntensity, on the order of saturation intensity]
-
A.
laserEnergyPerShot
Indicates the amount of energy released by a laser in a single shot or pulse.
-
B.
laserColor
Indicates the color attribute associated with a laser in the relationship or action.
-
C.
usedBeamIntensity
chosen
Indicates that a specific beam intensity value was applied or utilized in a given process, experiment, or operation.
-
D.
usesLaserType
Indicates that one entity employs or operates a specific type or category of laser in performing an action or function.
-
E.
usesLaser
Indicates that one entity employs or operates a laser in relation to another entity or 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_69f348e60748819082dcaa7792659803 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a037c894b488190bcbec2eccaff4a01 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e7aa0c8190bdc9ee4d54fc821b |
completed | May 12, 2026, 7:05 p.m. |
Created at: April 30, 2026, 11:39 p.m.