Triple
T21046836
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sperner family |
E518470
|
entity |
| Predicate | optimizationQuestion |
P142612
|
FINISHED |
| Object | determine maximum size for given ground set size |
—
|
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: determine maximum size for given ground set size | Statement: [Sperner family, optimizationQuestion, determine maximum size for given ground set size]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: optimizationQuestion Context triple: [Sperner family, optimizationQuestion, determine maximum size for given ground set size]
-
A.
optimizationType
Indicates the specific strategy or method used to improve performance or efficiency within a given process or system.
-
B.
optimizationDomain
Indicates the domain, field, or context within which an optimization process or optimization-related activity is applied.
-
C.
optimizationRole
Indicates the role or function an entity plays within an optimization process or strategy.
-
D.
optimize
Indicates improving a process, system, or outcome to achieve the best possible performance or efficiency under given constraints.
-
E.
optimizationTarget
Indicates that one entity is the goal or objective that another entity is trying to improve, optimize, or make more efficient.
- 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_69e0b50438e08190917e2538bb8bc034 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fcf4d26481908b639996500a8319 |
completed | April 21, 2026, 4:28 a.m. |
| PD | Predicate disambiguation | batch_69e5dbf6728881908a2a43a5c8804a2a |
completed | April 20, 2026, 7:55 a.m. |
| PDg | Predicate description generation | batch_69e5e2df1a888190b5b478e76bdf7fdf |
completed | April 20, 2026, 8:25 a.m. |
Created at: April 16, 2026, 2:34 p.m.