Triple
T25550010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mordell curve |
E640411
|
entity |
| Predicate | hasAffineModel |
P159771
|
FINISHED |
| Object | y^2 = x^3 + k |
—
|
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: y^2 = x^3 + k | Statement: [Mordell curve, hasAffineModel, y^2 = x^3 + k]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAffineModel Context triple: [Mordell curve, hasAffineModel, y^2 = x^3 + k]
-
A.
hasShapeModel
Indicates that an entity is associated with a specific geometric or structural shape model that represents its form.
-
B.
hasAffiliationModel
Indicates that one entity uses, follows, or is governed by a particular affiliation model in its relationships or organizational structure.
-
C.
hasRealModel
Indicates that an abstract, theoretical, or simplified entity is associated with a corresponding concrete or physically instantiated model in the real world.
-
D.
hasDegreeOfFreedom
Indicates that one entity possesses a specific independent parameter or mode in which it can vary or move relative to another entity or within a system.
-
E.
hasAccessModel
Indicates that one entity is permitted to use, interact with, or retrieve a particular model controlled by another entity or system.
- 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_69e75dc101a881909fd33b02174e9768 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f8c583e48190a2a1f65d80a2b589 |
completed | May 2, 2026, 1:14 p.m. |
| PD | Predicate disambiguation | batch_69f5afec3e94819080d9ba86cf8c866e |
completed | May 2, 2026, 8:03 a.m. |
| PDg | Predicate description generation | batch_69f5f6b32a8881909baa0db57b80d56a |
completed | May 2, 2026, 1:05 p.m. |
Created at: April 21, 2026, 3:36 p.m.