Triple
T25499863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | short integer solution problem |
E639078
|
entity |
| Predicate | hardnessType |
P158601
|
FINISHED |
| Object | worst-case to average-case reduction |
—
|
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: worst-case to average-case reduction | Statement: [short integer solution problem, hardnessType, worst-case to average-case reduction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hardnessType Context triple: [short integer solution problem, hardnessType, worst-case to average-case reduction]
-
A.
hardness
Indicates the degree to which one entity resists being scratched, indented, or deformed by another.
-
B.
showsHardnessOf
Indicates that something displays or reveals the degree of hardness possessed by another entity.
-
C.
hardnessMohs
Indicates the relative hardness of a material as measured on the Mohs scale of mineral hardness.
-
D.
impliesHardness
Indicates that one entity suggests, entails, or leads to the conclusion that another entity possesses hardness.
-
E.
hardiness
Indicates the degree to which an entity can withstand or endure harsh, adverse, or challenging conditions.
- 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_69e75dbbd2a88190b70e1e645de14b9a |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f7ad6bf881909d335be043a00242 |
completed | May 2, 2026, 1:10 p.m. |
| PD | Predicate disambiguation | batch_69f468421ba08190880eac99135e5970 |
completed | May 1, 2026, 8:45 a.m. |
| PDg | Predicate description generation | batch_69f46d361c348190b5fdfd805ecde01b |
completed | May 1, 2026, 9:07 a.m. |
Created at: April 21, 2026, 2:42 p.m.