Triple
T14721014
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valiant–Vazirani theorem |
E345812
|
entity |
| Predicate | showsHardnessOf |
P115516
|
FINISHED |
| Object |
Unique-SAT
Unique-SAT is a specialized version of the Boolean satisfiability problem where instances are guaranteed to have at most one satisfying assignment, and it plays a central role in complexity theory due to its connections to randomness and NP-completeness.
|
E1115574
|
NE FINISHED |
How this triple was built (5 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: Unique-SAT | Statement: [Valiant–Vazirani theorem, showsHardnessOf, Unique-SAT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Unique-SAT Context triple: [Valiant–Vazirani theorem, showsHardnessOf, Unique-SAT]
-
A.
3-SAT
3-SAT is a classic Boolean satisfiability problem where each clause has exactly three literals and which serves as a fundamental NP-complete benchmark in computational complexity theory.
-
B.
Max-3-SAT
Max-3-SAT is an optimization variant of the Boolean satisfiability problem where the goal is to maximize the number of satisfied clauses, each containing exactly three literals, and it serves as a central problem in the study of approximation algorithms and hardness of approximation.
-
C.
Max-SAT
Max-SAT is the optimization variant of the Boolean satisfiability problem in which the goal is to find an assignment that satisfies the maximum possible number of clauses, making it a central problem in approximation algorithms and complexity theory.
-
D.
“Inapproximability results for SAT and other problems”
“Inapproximability results for SAT and other problems” is a seminal theoretical computer science paper by Johan Håstad that establishes tight hardness-of-approximation bounds for satisfiability and related optimization problems using probabilistically checkable proofs.
-
E.
TNTSAT
TNTSAT is a French free-to-air satellite television platform that broadcasts the national digital terrestrial TV channels via satellite.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Unique-SAT Triple: [Valiant–Vazirani theorem, showsHardnessOf, Unique-SAT]
Generated description
Unique-SAT is a specialized version of the Boolean satisfiability problem where instances are guaranteed to have at most one satisfying assignment, and it plays a central role in complexity theory due to its connections to randomness and NP-completeness.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Unique-SAT Target entity description: Unique-SAT is a specialized version of the Boolean satisfiability problem where instances are guaranteed to have at most one satisfying assignment, and it plays a central role in complexity theory due to its connections to randomness and NP-completeness.
-
A.
3-SAT
3-SAT is a classic Boolean satisfiability problem where each clause has exactly three literals and which serves as a fundamental NP-complete benchmark in computational complexity theory.
-
B.
Max-3-SAT
Max-3-SAT is an optimization variant of the Boolean satisfiability problem where the goal is to maximize the number of satisfied clauses, each containing exactly three literals, and it serves as a central problem in the study of approximation algorithms and hardness of approximation.
-
C.
Max-SAT
Max-SAT is the optimization variant of the Boolean satisfiability problem in which the goal is to find an assignment that satisfies the maximum possible number of clauses, making it a central problem in approximation algorithms and complexity theory.
-
D.
“Inapproximability results for SAT and other problems”
“Inapproximability results for SAT and other problems” is a seminal theoretical computer science paper by Johan Håstad that establishes tight hardness-of-approximation bounds for satisfiability and related optimization problems using probabilistically checkable proofs.
-
E.
TNTSAT
TNTSAT is a French free-to-air satellite television platform that broadcasts the national digital terrestrial TV channels via satellite.
- F. None of above. chosen
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: showsHardnessOf Context triple: [Valiant–Vazirani theorem, showsHardnessOf, Unique-SAT]
-
A.
hardness
Indicates the degree to which one entity resists being scratched, indented, or deformed by another.
-
B.
hardnessMohs
Indicates the relative hardness of a material as measured on the Mohs scale of mineral hardness.
-
C.
stoneQuality
Indicates the quality or grade assigned to a stone in terms of its characteristics or condition.
-
D.
hardiness
Indicates the degree to which an entity can withstand or endure harsh, adverse, or challenging conditions.
-
E.
hasMineral
Indicates that one entity contains, includes, or is composed of a specified mineral.
- F. None of above. chosen
Provenance (7 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_69d822e5911c8190ba589f957dbd9ba7 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec25d56fc8190871873ca55d49272 |
completed | April 14, 2026, 10:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdf0957bb081908f1f382f3be8ec20 |
completed | May 8, 2026, 2:17 p.m. |
| NEDg | Description generation | batch_69fdf440a03c8190886119ab3c8ab610 |
completed | May 8, 2026, 2:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fdf4f2acbc8190b51ee456093a2813 |
completed | May 8, 2026, 2:36 p.m. |
| PD | Predicate disambiguation | batch_69de657e174481909da0437556334a04 |
completed | April 14, 2026, 4:04 p.m. |
| PDg | Predicate description generation | batch_69de716d3aac8190aaa6dc1f099b86e8 |
completed | April 14, 2026, 4:55 p.m. |
Created at: April 10, 2026, 1:29 a.m.