Triple
T7150442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milstein method |
E166677
|
entity |
| Predicate | timeDiscretization |
P75141
|
FINISHED |
| Object | one-step method |
—
|
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: one-step method | Statement: [Milstein method, timeDiscretization, one-step method]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeDiscretization Context triple: [Milstein method, timeDiscretization, one-step method]
-
A.
timeDiscretizationFormula
Indicates a relationship where a specific mathematical formula is used to convert or approximate continuous time into discrete time steps for analysis or computation.
-
B.
timeSampling
Indicates that one entity specifies how or at what intervals another entity is sampled or measured over time.
-
C.
timeContinuousOrDiscrete
Indicates whether the time dimension in a given context is modeled as a continuous flow or as discrete, separate time points.
-
D.
timeScaleType
Indicates the type or category of temporal scaling applied to an event, process, or measurement (e.g., real-time, accelerated, aggregated).
-
E.
timeScaleUnit
Indicates the unit of temporal measurement (such as seconds, minutes, or hours) used to express a given time scale.
- 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_69c68886779c8190a8e3fbabffe68253 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e7f28b188190b1732ca711666531 |
completed | March 27, 2026, 8:26 p.m. |
| PD | Predicate disambiguation | batch_69c6e1caf4e48190b47bb398a3c1554d |
completed | March 27, 2026, 8 p.m. |
| PDg | Predicate description generation | batch_69c6e4a213508190a40aca39f9eee7d5 |
completed | March 27, 2026, 8:12 p.m. |
Created at: March 27, 2026, 2:46 p.m.