Triple
T26992510
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 3-SAT |
E679892
|
entity |
| Predicate | optimizationVariant |
P161753
|
FINISHED |
| Object | MAX-3-SAT |
—
|
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: MAX-3-SAT | Statement: [3-SAT, optimizationVariant, MAX-3-SAT]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: optimizationVariant Context triple: [3-SAT, optimizationVariant, MAX-3-SAT]
-
A.
optimizationTarget
Indicates that one entity is the goal or objective that another entity is trying to improve, optimize, or make more efficient.
-
B.
optimizationType
Indicates the specific strategy or method used to improve performance or efficiency within a given process or system.
-
C.
algorithmVariant
Indicates that one algorithm is a variant or modified version of another algorithm.
-
D.
exportVariantOf
Indicates that one entity is an exported version or externally released form derived from another, original entity.
-
E.
performanceVariantEngine
Indicates that one engine is a higher-performance variant or upgraded version of another engine.
- 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_69eeeb5138ac8190b3c273ddc659a54f |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f621915ee48190b508025afa2fb4fc |
completed | May 2, 2026, 4:08 p.m. |
| PD | Predicate disambiguation | batch_69f61b3ee7b08190a0a1bc5d26b757aa |
completed | May 2, 2026, 3:41 p.m. |
| PDg | Predicate description generation | batch_69f61f109ef48190873bfe18638d2046 |
completed | May 2, 2026, 3:58 p.m. |
Created at: April 27, 2026, 6:52 a.m.