Triple
T15522125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | XXZ spin chain |
E368993
|
entity |
| Predicate | hasAnisotropy |
P118997
|
FINISHED |
| Object | anisotropic spin–spin coupling |
—
|
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: anisotropic spin–spin coupling | Statement: [XXZ spin chain, hasAnisotropy, anisotropic spin–spin coupling]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAnisotropy Context triple: [XXZ spin chain, hasAnisotropy, anisotropic spin–spin coupling]
-
A.
isIsotropic
Indicates that a property or behavior is identical in all directions, showing no directional dependence.
-
B.
supportsMultisampling
Indicates that an entity provides or enables multisampling functionality, typically for improved rendering quality.
-
C.
hasDensityContrast
Indicates that one entity differs from another in material density, highlighting a contrast in how compact or dense they are.
-
D.
hasSkewness
Indicates that a distribution or dataset exhibits a specific degree and direction of asymmetry around its central value.
-
E.
hasGradient
Indicates that one entity possesses or is characterized by a gradual change in value, intensity, or property across its extent or between two points.
- 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_69d85a1794cc8190b0b428716296e63e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0403543188190abac49d2b9decb89 |
completed | April 16, 2026, 1:49 a.m. |
| PD | Predicate disambiguation | batch_69ded28ab0588190a47a9090d1238707 |
completed | April 14, 2026, 11:49 p.m. |
| PDg | Predicate description generation | batch_69ded57165288190979b7acb71ad5145 |
completed | April 15, 2026, 12:01 a.m. |
Created at: April 10, 2026, 4:04 a.m.