Triple
T7115517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | OBDA systems |
E165807
|
entity |
| Predicate | optimizeFor |
P33716
|
FINISHED |
| Object | first-order rewritability of queries |
—
|
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: first-order rewritability of queries | Statement: [OBDA systems, optimizeFor, first-order rewritability of queries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: optimizeFor Context triple: [OBDA systems, optimizeFor, first-order rewritability of queries]
-
A.
optimize
Indicates improving a process, system, or outcome to achieve the best possible performance or efficiency under given constraints.
-
B.
optimizationType
Indicates the specific strategy or method used to improve performance or efficiency within a given process or system.
-
C.
optimizationTarget
chosen
Indicates that one entity is the goal or objective that another entity is trying to improve, optimize, or make more efficient.
-
D.
optimizationLevel
Indicates the degree or intensity to which a process, system, or solution has been refined to improve its performance or efficiency.
-
E.
optimizationStyle
Indicates the particular method or approach used to optimize a process, system, or solution.
- 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_69c6888227bc8190a1394679e3116f90 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e5f401b881909ef4c2ab1e0750db |
completed | March 27, 2026, 8:17 p.m. |
| PD | Predicate disambiguation | batch_69c6e1c4f9788190830288d00cc37026 |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:43 p.m.