Triple
T8449068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Molecular computation of solutions to combinatorial problems |
E199754
|
entity |
| Predicate | problemType |
P83422
|
FINISHED |
| Object | combinatorial search 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: combinatorial search problem | Statement: [Molecular computation of solutions to combinatorial problems, problemType, combinatorial search problem]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: problemType Context triple: [Molecular computation of solutions to combinatorial problems, problemType, combinatorial search problem]
-
A.
problemStatement
Indicates that an entity presents, defines, or expresses a specific problem or issue to be addressed.
-
B.
problems
Indicates that one entity has issues, difficulties, or complications associated with or caused by another entity.
-
C.
numberOfProblems
Indicates the quantity or count of problems associated with a given entity or situation.
-
D.
hasWordProblem
Indicates that an entity (such as a mathematical concept, operation, or topic) is associated with or can be expressed through a word problem.
-
E.
solutionType
Indicates the specific category or kind of solution associated with an entity or problem.
- 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_69ca83170f9081909cd98f55614c6476 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe445b7988190b53ae45070c70d1d |
completed | March 31, 2026, 3:12 p.m. |
| PD | Predicate disambiguation | batch_69cbd0f5a3648190beb53a139a2d5482 |
completed | March 31, 2026, 1:49 p.m. |
| PDg | Predicate description generation | batch_69cbe30c2d088190b4cb89adb4e88273 |
completed | March 31, 2026, 3:06 p.m. |
Created at: March 30, 2026, 6:09 p.m.