Triple
T15726310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frygt og Bæven |
E381225
|
entity |
| Predicate | numberOfProblemata |
P119945
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Frygt og Bæven, numberOfProblemata, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfProblemata Context triple: [Frygt og Bæven, numberOfProblemata, 3]
-
A.
numberOfProblems
Indicates the quantity or count of problems associated with a given entity or situation.
-
B.
problemTypeSolved
Indicates that a given problem has been successfully solved or resolved by a particular entity or method.
-
C.
numberOfMillenniumProblems
Indicates the total count of Millennium Problems associated with a given subject or context.
-
D.
problemType
Indicates the specific category or classification of a problem within a defined problem space or system.
-
E.
typicalProblem
Indicates that a situation, issue, or obstacle is representative or characteristic of the usual problems encountered in a given context.
- 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04fb357a88190a92641c8a8c20573 |
completed | April 16, 2026, 2:55 a.m. |
| PD | Predicate disambiguation | batch_69e00526759c819088b80d85138b8974 |
completed | April 15, 2026, 9:37 p.m. |
| PDg | Predicate description generation | batch_69e0094af5b481908ad51d5d7ba0c726 |
completed | April 15, 2026, 9:55 p.m. |
Created at: April 10, 2026, 4:46 a.m.