Triple
T26966774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NL |
E679191
|
entity |
| Predicate | configurationGraphSize |
P52889
|
FINISHED |
| Object | polynomial in input 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: polynomial in input size | Statement: [NL, configurationGraphSize, polynomial in input size]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: configurationGraphSize Context triple: [NL, configurationGraphSize, polynomial in input size]
-
A.
gridSize
Indicates the dimensions or scale of a grid used to structure or partition a space or dataset.
-
B.
nodeNumberSize
Indicates the size or magnitude associated with a node’s numerical identifier or value.
-
C.
numberOfConfigurations
chosen
Indicates the total count of distinct configurations associated with or applicable to a given entity or situation.
-
D.
graphicsDimension
Indicates a relationship where an entity has or is associated with a specific graphical size or dimensional properties.
-
E.
modelSize
Indicates the quantitative measure of how large or complex a model is, typically in terms of parameters, layers, or memory footprint.
- 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_69eeeb4f3a448190b1e94b2d4776c16e |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f661b58ac48190907b6c6e9ccc2c59 |
completed | May 2, 2026, 8:42 p.m. |
| PD | Predicate disambiguation | batch_69f660eea4648190b0d5e24293607813 |
completed | May 2, 2026, 8:39 p.m. |
Created at: April 27, 2026, 6:36 a.m.