Triple
T25550029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mordell curve |
E640411
|
entity |
| Predicate | hasRealPoints |
P152471
|
FINISHED |
| Object | solutions (x,y) ∈ ℝ² of 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: solutions (x,y) ∈ ℝ² of y^2 = x^3 + k | Statement: [Mordell curve, hasRealPoints, solutions (x,y) ∈ ℝ² of y^2 = x^3 + k]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRealPoints Context triple: [Mordell curve, hasRealPoints, solutions (x,y) ∈ ℝ² of y^2 = x^3 + k]
-
A.
isRealPointsOf
chosen
Indicates that one entity represents the real-valued points or real-number coordinates associated with another entity.
-
B.
hasComplexPoints
Indicates that something possesses or includes points that are intricate, detailed, or composed of multiple interconnected parts.
-
C.
hasRealEmbeddings
Indicates that an entity is represented or can be represented using real-valued vector embeddings in some vector space.
-
D.
hasRealComponents
Indicates that an entity possesses components or parts whose values are real (non-complex) numbers.
-
E.
hasNumberOfPoints
Indicates that an entity is associated with a specific count of points it possesses or comprises.
- 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_69e75dc101a881909fd33b02174e9768 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69fb2e940d5c8190bceae77daf4ef512 |
completed | May 6, 2026, 12:05 p.m. |
| PD | Predicate disambiguation | batch_69f9fec70bd881909c658a3c5020318b |
completed | May 5, 2026, 2:29 p.m. |
Created at: April 21, 2026, 3:36 p.m.