Triple
T2325286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ornstein–Uhlenbeck process |
E48273
|
entity |
| Predicate | hasDriftForm |
P38107
|
FINISHED |
| Object | linear drift toward long-term mean |
—
|
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: linear drift toward long-term mean | Statement: [Ornstein–Uhlenbeck process, hasDriftForm, linear drift toward long-term mean]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDriftForm Context triple: [Ornstein–Uhlenbeck process, hasDriftForm, linear drift toward long-term mean]
-
A.
hasHistoricalShiftFrom
Indicates a relationship where one state, practice, or condition has been replaced or transformed over time from another earlier state, practice, or condition.
-
B.
hasDivergence
Indicates that there is a difference, deviation, or separation between two otherwise related entities, states, or paths.
-
C.
hasDelta
Indicates that there is a change, difference, or deviation between two related states, values, or versions of something.
-
D.
hasHistoricalShiftTo
Indicates a change over time in which one state, condition, or configuration is replaced or transformed into another in a historically traceable way.
-
E.
hasPetForm
Indicates that one entity can transform into or assume the form of another entity that is characterized as a pet.
- 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_69a88aa308a88190b0b86c011fda7fce |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc685f05481909c863b29d1f6bacd |
completed | March 7, 2026, 6:32 a.m. |
| PD | Predicate disambiguation | batch_69abc5909cc48190aab257313542dc49 |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abc682d094819081a96ffb77c4c42a |
completed | March 7, 2026, 6:32 a.m. |
Created at: March 4, 2026, 7:50 p.m.