Triple
T34674120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lax–Wendroff method |
E890451
|
entity |
| Predicate | discretizationType |
P141098
|
FINISHED |
| Object | explicit scheme |
—
|
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: explicit scheme | Statement: [Lax–Wendroff method, discretizationType, explicit scheme]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: discretizationType Context triple: [Lax–Wendroff method, discretizationType, explicit scheme]
-
A.
discretizes
Indicates that one entity converts or partitions another entity into a finite set of discrete parts, intervals, or categories.
-
B.
timeDiscretization
Indicates how a continuous or overall time period is divided into discrete intervals or steps for representation or processing.
-
C.
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.
-
D.
quantizationType
chosen
Indicates the specific method or scheme used to discretize continuous values into quantized levels.
-
E.
dispersionType
Indicates the manner or pattern in which something is spread, scattered, or distributed relative to something else.
- 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_69f349d9c59481908b36baa0be093aea |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f72322256c8190afb14d73a2612b6f |
completed | May 3, 2026, 10:27 a.m. |
| PD | Predicate disambiguation | batch_69f72157af108190880317a62e634bb0 |
completed | May 3, 2026, 10:20 a.m. |
Created at: May 1, 2026, 2:05 a.m.