Triple
T25433417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pólya’s conjecture |
E637315
|
entity |
| Predicate | hasCounterexamples |
P64864
|
FINISHED |
| Object | values of x for which M(x) > 0 |
—
|
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: values of x for which M(x) > 0 | Statement: [Pólya’s conjecture, hasCounterexamples, values of x for which M(x) > 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCounterexamples Context triple: [Pólya’s conjecture, hasCounterexamples, values of x for which M(x) > 0]
-
A.
hasCounterexample
chosen
Indicates that there exists at least one specific case or instance that disproves or violates a given claim, rule, or general statement.
-
B.
hasCounterexampleStatus
Indicates whether a given statement, conjecture, or claim has been shown to have a counterexample (and possibly what that status is).
-
C.
hasCounterexampleYear
Indicates the year in which a counterexample to a claim, conjecture, or statement was found or demonstrated.
-
D.
hasNonExample
Indicates that something is associated with an instance that explicitly does not satisfy or illustrate a given concept, rule, or category.
-
E.
hasExample
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
- 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_69e75db6c97081908178383fa632b193 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f78c61ed4c8190ad84c918fa9af55a |
completed | May 3, 2026, 5:56 p.m. |
| PD | Predicate disambiguation | batch_69f78b8cb3a881909ebaac1b503988c2 |
completed | May 3, 2026, 5:53 p.m. |
Created at: April 21, 2026, 1:59 p.m.