Triple
T27707921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ICS |
E698601
|
entity |
| Predicate | calibrationGoal |
P162969
|
FINISHED |
| Object | target level of solvency for insurance groups |
—
|
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: target level of solvency for insurance groups | Statement: [ICS, calibrationGoal, target level of solvency for insurance groups]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: calibrationGoal Context triple: [ICS, calibrationGoal, target level of solvency for insurance groups]
-
A.
calibratedFor
Indicates that something has been adjusted or tuned to operate accurately or optimally for a specific target, condition, or context.
-
B.
calibratedIn
Indicates that an instrument, device, or measurement process has been adjusted or verified to ensure accuracy according to a specified standard, environment, or unit system.
-
C.
hasCalibration
Indicates that an entity is associated with or uses a specific calibration configuration, setting, or procedure.
-
D.
machineGoal
Indicates that a machine or automated system has a specific objective, target state, or outcome it is intended or programmed to achieve.
-
E.
usedToCalibrate
Indicates that one entity serves as a reference or standard to adjust, tune, or verify the accuracy of another entity.
- 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_69ef590f655c81909f93893b3b3219b2 |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69f635c9422c8190bc1ba40260b41fbc |
completed | May 2, 2026, 5:35 p.m. |
| PD | Predicate disambiguation | batch_69f62c1a92648190835a2c5250d8c758 |
completed | May 2, 2026, 4:53 p.m. |
| PDg | Predicate description generation | batch_69f6305219f08190b55f6193a4a49984 |
completed | May 2, 2026, 5:11 p.m. |
Created at: April 27, 2026, 3 p.m.