Triple
T19377352
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coulomb gap |
E484704
|
entity |
| Predicate | inThreeDimensions |
P95204
|
FINISHED |
| Object | density of states proportional to E^2 near Fermi level |
—
|
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: density of states proportional to E^2 near Fermi level | Statement: [Coulomb gap, inThreeDimensions, density of states proportional to E^2 near Fermi level]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inThreeDimensions Context triple: [Coulomb gap, inThreeDimensions, density of states proportional to E^2 near Fermi level]
-
A.
in3Dimensions
chosen
Indicates that something exists, occurs, or is represented within three-dimensional space.
-
B.
formationDimension
Indicates the dimensional characteristics (such as size, scale, or extent) associated with the formation of something.
-
C.
in2Dimensions
Indicates that one entity is located or exists within the two-dimensional spatial extent defined by another entity.
-
D.
has3DVersion
Indicates that an entity has a corresponding three-dimensional (3D) version or representation.
-
E.
stereoscopic3D
Indicates that the subject is presented or perceived using stereoscopic 3D techniques, creating a depth effect by delivering slightly different images to each eye.
- 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_69d8e8d460d88190abf0591c5c9d2b0c |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e61a5cfbf48190ac60e3ffa6baa263 |
completed | April 20, 2026, 12:21 p.m. |
| PD | Predicate disambiguation | batch_69e4fd54f8e48190956e73dd8969164a |
completed | April 19, 2026, 4:05 p.m. |
Created at: April 10, 2026, 1:35 p.m.