Triple
T21088249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reducibility Among Combinatorial Problems |
E519559
|
entity |
| Predicate | establishesNPCompletenessOf |
P142028
|
FINISHED |
| Object | Set Covering problem |
—
|
NE NERFINISHED |
How this triple was built (3 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: Set Covering problem | Statement: [Reducibility Among Combinatorial Problems, establishesNPCompletenessOf, Set Covering problem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Set Covering problem Context triple: [Reducibility Among Combinatorial Problems, establishesNPCompletenessOf, Set Covering problem]
-
A.
Packing and Covering
"Packing and Covering" is a classic mathematical monograph by C. A. Rogers that systematically develops the theory of packing and covering problems in geometry and number theory.
-
B.
Clique problem
The Clique problem is a classic NP-complete decision problem in graph theory that asks whether a graph contains a fully connected subgraph (clique) of at least a given size.
-
C.
Subset sum problem
The subset sum problem is a classic NP-complete decision problem in computer science that asks whether any subset of given integers sums to a specified target value.
-
D.
Cover’s theorem
Cover’s theorem is a result in statistical pattern recognition stating that data cast nonlinearly into a higher-dimensional space is more likely to be linearly separable than in a lower-dimensional space.
-
E.
Steiner tree problem
The Steiner tree problem is a classic optimization problem in combinatorial mathematics and computer science that seeks the shortest network of line segments connecting a given set of points, potentially adding extra intermediate points to minimize total length.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Set Covering problem Target entity description: The Set Covering problem is a classic NP-complete combinatorial optimization problem that asks for the smallest collection of sets whose union covers all elements in a given universe.
-
A.
Packing and Covering
"Packing and Covering" is a classic mathematical monograph by C. A. Rogers that systematically develops the theory of packing and covering problems in geometry and number theory.
-
B.
Clique problem
The Clique problem is a classic NP-complete decision problem in graph theory that asks whether a graph contains a fully connected subgraph (clique) of at least a given size.
-
C.
Subset sum problem
The subset sum problem is a classic NP-complete decision problem in computer science that asks whether any subset of given integers sums to a specified target value.
-
D.
Cover’s theorem
Cover’s theorem is a result in statistical pattern recognition stating that data cast nonlinearly into a higher-dimensional space is more likely to be linearly separable than in a lower-dimensional space.
-
E.
Steiner tree problem
The Steiner tree problem is a classic optimization problem in combinatorial mathematics and computer science that seeks the shortest network of line segments connecting a given set of points, potentially adding extra intermediate points to minimize total length.
- F. None of above. chosen
Provenance (2 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_69e0b507dd9081908fb8bfcbef4c8b46 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7094cebe08190bb10f51a45c244ec |
completed | April 21, 2026, 5:21 a.m. |
Created at: April 16, 2026, 2:50 p.m.