Triple
T13031867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Néel wall |
E326460
|
entity |
| Predicate | hasMagnetizationRotation |
P94677
|
FINISHED |
| Object | within the plane of the material |
—
|
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: within the plane of the material | Statement: [Néel wall, hasMagnetizationRotation, within the plane of the material]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMagnetizationRotation Context triple: [Néel wall, hasMagnetizationRotation, within the plane of the material]
-
A.
hasMagneticMoment
Indicates that an entity possesses a magnetic moment, characterizing the strength and orientation of its magnetism.
-
B.
hasRotation
chosen
Indicates that one entity is rotated with respect to another, or possesses a specific rotational orientation or motion.
-
C.
magnetizationProportionalTo
Indicates that the magnetization of one entity varies in direct proportion to a specified quantity or property of another entity.
-
D.
expressesMagnetizationWith
Indicates that one entity represents or encodes the magnetization state or properties of another entity.
-
E.
hasMagnetStatus
Indicates that an entity possesses a specific magnet-related state or designation within a given 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97efe72348190b52fb4068f5fb829 |
completed | April 10, 2026, 10:51 p.m. |
| PD | Predicate disambiguation | batch_69d97dc39a0881908119c62e31bf6182 |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 8:54 p.m.