Triple
T31792936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doppler cooling |
E811517
|
entity |
| Predicate | typicalLaserDetuning |
P207517
|
FINISHED |
| Object | a few natural linewidths below resonance |
—
|
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: a few natural linewidths below resonance | Statement: [Doppler cooling, typicalLaserDetuning, a few natural linewidths below resonance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalLaserDetuning Context triple: [Doppler cooling, typicalLaserDetuning, a few natural linewidths below resonance]
-
A.
wavelengthOfReadingLaser
Indicates the specific wavelength at which the reading laser operates during a reading or scanning process.
-
B.
discoveredAsLaserMedium
Indicates that something was identified or recognized as a suitable medium for generating laser action.
-
C.
usesLaserType
Indicates that one entity employs or operates a specific type or category of laser in performing an action or function.
-
D.
laserColor
Indicates the color attribute associated with a laser in the relationship or action.
-
E.
usesLaser
Indicates that one entity employs or operates a laser in relation to another entity or context.
- F. None of above. chosen
Provenance (4 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_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e7aa0c8190bdc9ee4d54fc821b |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7ee0388190a29faeb5cdb0950a |
completed | May 12, 2026, 7:16 p.m. |
Created at: April 30, 2026, 11:39 p.m.