Triple
T31593514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RDX |
E806162
|
entity |
| Predicate | hasRelativeEffectivenessFactor |
P84339
|
FINISHED |
| Object | about 1.60 |
—
|
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: about 1.60 | Statement: [RDX, hasRelativeEffectivenessFactor, about 1.60]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelativeEffectivenessFactor Context triple: [RDX, hasRelativeEffectivenessFactor, about 1.60]
-
A.
hasEffectIn
Indicates that one entity produces, causes, or exerts an effect within a specified context, system, or environment.
-
B.
effectivenessAgainst
chosen
Indicates how well one entity performs in countering, influencing, or mitigating the impact of another entity.
-
C.
hasRelativisticEffects
Indicates that the relationship or process involves velocities or gravitational fields high enough that relativistic (Einsteinian) corrections to classical physics become significant.
-
D.
alsoEffectiveAtDistance
Indicates that the same effect or action remains valid or functional even when applied at a distance from its original or primary location.
-
E.
areAffectedBy
Indicates that one entity experiences an effect, influence, or impact as a result of another entity or event.
- 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_69f348d4891c8190b02bae3c8ecb68b7 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a017bc21b048190b9c680013de203b6 |
completed | May 11, 2026, 6:48 a.m. |
| PD | Predicate disambiguation | batch_6a017aa09c4481909c3b55e0cd13501e |
completed | May 11, 2026, 6:43 a.m. |
Created at: April 30, 2026, 10:29 p.m.