Triple
T24824657
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | European XFEL |
E621154
|
entity |
| Predicate | hasXrayWavelengthRange |
P45250
|
FINISHED |
| Object | soft to hard X-ray regime |
—
|
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: soft to hard X-ray regime | Statement: [European XFEL, hasXrayWavelengthRange, soft to hard X-ray regime]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasXrayWavelengthRange Context triple: [European XFEL, hasXrayWavelengthRange, soft to hard X-ray regime]
-
A.
hasXRaySources
Indicates that an entity possesses or is associated with one or more X-ray emitting sources.
-
B.
supportsWavelengthRange
chosen
Indicates that an entity is capable of operating over, handling, or being compatible with a specified range of wavelengths.
-
C.
usedWavelengthRange
Indicates the range of wavelengths that were employed or applied in performing a particular process, measurement, or operation.
-
D.
XRayLuminosity
Indicates the amount of energy an object emits in the form of X-ray radiation.
-
E.
approximateWavelengthRange
Indicates the range of wavelengths that approximately characterizes or bounds the phenomenon, object, or interaction in question.
- 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_69e2fac0c3b881909110e5a56c6fa46f |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f43043512481909501a3979cac9947 |
completed | May 1, 2026, 4:46 a.m. |
| PD | Predicate disambiguation | batch_69f420fd375c81908ea4a4e60b76ee8f |
completed | May 1, 2026, 3:41 a.m. |
Created at: April 18, 2026, 5:05 a.m.