Triple
T17196366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Economic Simplified Boiling Water Reactor |
E417360
|
entity |
| Predicate | coreDamageFrequency |
P126691
|
FINISHED |
| Object | very low compared to earlier designs |
—
|
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 compared to earlier designs | Statement: [Economic Simplified Boiling Water Reactor, coreDamageFrequency, very low compared to earlier designs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coreDamageFrequency Context triple: [Economic Simplified Boiling Water Reactor, coreDamageFrequency, very low compared to earlier designs]
-
A.
damageTo
Indicates a relationship where one entity causes harm, loss, or deterioration to another entity.
-
B.
damageLeadsTo
Indicates that one instance of damage causally results in or contributes to another specified outcome or condition.
-
C.
damageBasis
Indicates the underlying reason, cause, or basis on which damage is determined or assessed in a given context.
-
D.
damageEffect
Indicates that one entity causes harm, reduction, or deterioration to another entity or its properties.
-
E.
fireballFrequency
Indicates how often fireball events occur or are produced within a given context or time period.
- 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_69d886d6ba8c819093215917b3d01689 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42daab57c819093496cbdc7890f34 |
completed | April 19, 2026, 1:19 a.m. |
| PD | Predicate disambiguation | batch_69e383141ae0819096acd71683637cbc |
completed | April 18, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69e39c2fedb881908bfed2c3e5f2616a |
completed | April 18, 2026, 2:58 p.m. |
Created at: April 10, 2026, 5:38 a.m.