Triple
T1382158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gaussian distribution |
E29361
|
entity |
| Predicate | hasInflectionPointsAt |
P27176
|
FINISHED |
| Object | μ − σ |
—
|
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: μ − σ | Statement: [Gaussian distribution, hasInflectionPointsAt, μ − σ]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInflectionPointsAt Context triple: [Gaussian distribution, hasInflectionPointsAt, μ − σ]
-
A.
isConcaveIn
Indicates that a function or relation curves inward (is concave) with respect to a specified variable or argument, so that any line segment between two points on its graph lies below or on the graph.
-
B.
hasPeak
Indicates that something possesses or contains a highest point, summit, or maximum value.
-
C.
hasCurvatureDivergence
Indicates that one entity exhibits a difference or variation in curvature relative to another entity or reference.
-
D.
hasCurvatureInvariant
Indicates that one entity possesses a specific curvature-related invariant property or value associated with its geometric or mathematical structure.
-
E.
hadCrossingPoints
Indicates that two entities intersected or overlapped at one or more specific points in space or time.
- 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_69a498d883a48190bfdca525296ef7ee |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c3361bf08190b3f6bbf82e17685b |
completed | March 1, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69a4befe343c81909f758440a531b5be |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c0335f7081908d50046ced4cdee0 |
completed | March 1, 2026, 10:39 p.m. |
Created at: March 1, 2026, 7:59 p.m.