Triple
T3576705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bethe formula for stopping power |
E75705
|
entity |
| Predicate | lessAccurateFor |
P43797
|
FINISHED |
| Object | very low energy particles |
—
|
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: very low energy particles | Statement: [Bethe formula for stopping power, lessAccurateFor, very low energy particles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lessAccurateFor Context triple: [Bethe formula for stopping power, lessAccurateFor, very low energy particles]
-
A.
accuracyDependsOn
chosen
Indicates that the accuracy of one entity or process is contingent upon, or influenced by, another entity or factor.
-
B.
isLessEfficientThan
Indicates that one entity performs a task or uses resources with lower efficiency compared to another entity.
-
C.
notAdjustedFor
Indicates that a value, measure, or result has not been modified or corrected to account for certain factors, conditions, or variables.
-
D.
approximates
Indicates that one entity is close to, but not exactly equal to, the value, form, or behavior of another entity.
-
E.
lessWorthyThan
Indicates that one entity is considered to have lower value, merit, or worth compared to another entity.
- 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_69ad85d5e3008190bdfe0bacdd1f5a1b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0dba238819083a1d09005c312b8 |
completed | March 8, 2026, 6:32 p.m. |
| PD | Predicate disambiguation | batch_69adb83810c481909c645c08b978edc1 |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:21 p.m.