Triple
T8177737
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FERMI free-electron laser |
E190979
|
entity |
| Predicate | tunable |
P81276
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [FERMI free-electron laser, tunable, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tunable Context triple: [FERMI free-electron laser, tunable, true]
-
A.
tuning
Indicates the adjustment or calibration of something’s parameters or settings to achieve desired performance or behavior.
-
B.
tunedBy
Indicates that one entity has been adjusted or calibrated in its settings, parameters, or configuration by another entity.
-
C.
tuningMethod
Indicates the method or approach used to adjust or optimize something’s parameters or performance.
-
D.
tonal
Indicates that one entity has a tone, pitch pattern, or tonal quality in relation to another (such as a language, sound, or musical element).
-
E.
modulates
Indicates that one entity adjusts, regulates, or alters the intensity, frequency, or effect of another entity or process.
- 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_69ca82c4538081909404325aa5639483 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb4abb66bc81908d758c7af2e23ac6 |
completed | March 31, 2026, 4:16 a.m. |
| PD | Predicate disambiguation | batch_69cb36a7952481908f34e3e82f375a84 |
completed | March 31, 2026, 2:51 a.m. |
| PDg | Predicate description generation | batch_69cb45503eec8190aeef0da6c3324710 |
completed | March 31, 2026, 3:53 a.m. |
Created at: March 30, 2026, 5:40 p.m.