Triple
T17858527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cotton–Mouton effect |
E446002
|
entity |
| Predicate | governingQuantity |
P75768
|
FINISHED |
| Object | magnetic susceptibility anisotropy of the medium |
—
|
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: magnetic susceptibility anisotropy of the medium | Statement: [Cotton–Mouton effect, governingQuantity, magnetic susceptibility anisotropy of the medium]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: governingQuantity Context triple: [Cotton–Mouton effect, governingQuantity, magnetic susceptibility anisotropy of the medium]
-
A.
quantificationType
Indicates the specific kind or category of quantity or measurement being applied in a given context.
-
B.
governsTypeOfUnit
Indicates that one entity has authority or control over the type or category of unit to which another entity belongs.
-
C.
relatesToQuantity
Indicates a relationship where one entity is associated with, depends on, or is characterized by a specific quantity or amount.
-
D.
quantifies
Indicates that one entity expresses or specifies the amount, number, or degree of another entity.
-
E.
mainQuantity
chosen
Indicates that the associated value represents the primary or principal quantity in a given context or relationship.
- 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_69d8b9f26f18819089c9e43250bee6ae |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4978e68ec8190a4306f7b7bb058d7 |
completed | April 19, 2026, 8:51 a.m. |
| PD | Predicate disambiguation | batch_69e3d8e6d2e88190ad9ef9f8a99f13e6 |
completed | April 18, 2026, 7:17 p.m. |
Created at: April 10, 2026, 10:17 a.m.