Triple
T13031896
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Néel wall |
E326460
|
entity |
| Predicate | hasMagnetizationConfiguration |
P107545
|
FINISHED |
| Object | head-to-tail along the wall line |
—
|
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: head-to-tail along the wall line | Statement: [Néel wall, hasMagnetizationConfiguration, head-to-tail along the wall line]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMagnetizationConfiguration Context triple: [Néel wall, hasMagnetizationConfiguration, head-to-tail along the wall line]
-
A.
hasMagneticMoment
Indicates that an entity possesses a magnetic moment, characterizing the strength and orientation of its magnetism.
-
B.
hasMagnetStatus
Indicates that an entity possesses a specific magnet-related state or designation within a given context.
-
C.
expressesMagnetizationWith
Indicates that one entity represents or encodes the magnetization state or properties of another entity.
-
D.
magnetizationProportionalTo
Indicates that the magnetization of one entity varies in direct proportion to a specified quantity or property of another entity.
-
E.
hasMagneticChronCode
Indicates that an entity is associated with a specific magnetic chron code used to encode temporal or time-related information.
- 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_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. |
| PDg | Predicate description generation | batch_69d97e3df2288190a7f27d31d248bb7f |
completed | April 10, 2026, 10:48 p.m. |
Created at: April 9, 2026, 8:54 p.m.