Triple
T23006211
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Glauber coherent states |
E572775
|
entity |
| Predicate | meanPhotonNumber |
P150612
|
FINISHED |
| Object | ⟨n⟩ = |α|² |
—
|
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: ⟨n⟩ = |α|² | Statement: [Glauber coherent states, meanPhotonNumber, ⟨n⟩ = |α|²]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: meanPhotonNumber Context triple: [Glauber coherent states, meanPhotonNumber, ⟨n⟩ = |α|²]
-
A.
numberOfRays
Indicates the count of rays associated with or emitted by a given entity.
-
B.
laserEnergyPerShot
Indicates the amount of energy released by a laser in a single shot or pulse.
-
C.
totalLuminosity
Indicates the overall emitted light or radiant power produced by an entity, typically by summing or integrating the luminosity from all its parts or components.
-
D.
neutronEmissionRate
Indicates the rate at which neutrons are emitted from a source or system over a given period of time.
-
E.
pixelCount
Indicates the total number of individual pixels that make up a given image or visual element.
- 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_69e245b6a3ac81908087599eefe3e365 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1835706dc8190b3f9743c0f336bb2 |
completed | April 29, 2026, 4:04 a.m. |
| PD | Predicate disambiguation | batch_69ef3b9cd5488190bcd23183179f48cd |
completed | April 27, 2026, 10:34 a.m. |
| PDg | Predicate description generation | batch_69ef538b29c081908fa56ee35a1dcee7 |
completed | April 27, 2026, 12:16 p.m. |
Created at: April 17, 2026, 3:51 p.m.