Triple
T26992585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Subset sum problem |
E679894
|
entity |
| Predicate | reductionFrom |
P161754
|
FINISHED |
| Object | partition problem |
—
|
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: partition problem | Statement: [Subset sum problem, reductionFrom, partition problem]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reductionFrom Context triple: [Subset sum problem, reductionFrom, partition problem]
-
A.
reductionFrom
chosen
Indicates that one entity is derived by simplifying, decreasing, or transforming another entity, typically resulting in a smaller, less complex, or less resource-intensive form.
-
B.
reductionFrom
Indicates that one entity is derived by simplifying, decreasing, or transforming another entity, typically resulting in a smaller, less complex, or less costly version.
-
C.
reductionTo
Indicates that one entity is transformed, simplified, or mapped into another entity that is considered an equivalent or simpler form, often preserving essential properties.
-
D.
reduction
Indicates a relationship where something is decreased in amount, size, intensity, or degree compared to a prior state or reference.
-
E.
reduces
Indicates that one entity causes a decrease in the amount, intensity, degree, or impact 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_69eeeb5138ac8190b3c273ddc659a54f |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f6383625cc8190aa223d8ef655743c |
completed | May 2, 2026, 5:45 p.m. |
| PD | Predicate disambiguation | batch_69f63709e4848190b5cf322e06b23fb6 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 27, 2026, 6:52 a.m.