Triple
T23269472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DAMA/LIBRA |
E588247
|
entity |
| Predicate | usesCalibration |
P98657
|
FINISHED |
| Object | gamma-ray sources for energy calibration |
—
|
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: gamma-ray sources for energy calibration | Statement: [DAMA/LIBRA, usesCalibration, gamma-ray sources for energy calibration]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesCalibration Context triple: [DAMA/LIBRA, usesCalibration, gamma-ray sources for energy calibration]
-
A.
hasCalibration
Indicates that an entity is associated with or uses a specific calibration configuration, setting, or procedure.
-
B.
usedToCalibrate
chosen
Indicates that one entity serves as a reference or standard to adjust, tune, or verify the accuracy of another entity.
-
C.
supportsHardwareCalibration
Indicates that one entity provides the capability or functionality to perform calibration operations on another entity’s hardware.
-
D.
calibratedFor
Indicates that something has been adjusted or tuned to operate accurately or optimally for a specific target, condition, or context.
-
E.
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.
- 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_69e25d148adc819088efbf42672604e9 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1957219188190b30bceffad1542da |
completed | April 29, 2026, 5:21 a.m. |
| PD | Predicate disambiguation | batch_69effcecabd88190856fb6e1d993e4dd |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:45 p.m.