Triple
T27107161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Westinghouse 3-loop PWR |
E686612
|
entity |
| Predicate | primaryCoolantType |
P109382
|
FINISHED |
| Object | light water |
—
|
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: light water | Statement: [Westinghouse 3-loop PWR, primaryCoolantType, light water]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryCoolantType Context triple: [Westinghouse 3-loop PWR, primaryCoolantType, light water]
-
A.
usesBoronInCoolant
Indicates that boron is employed as a component or additive in the coolant used in a system or process.
-
B.
hasReactorCoolantSystem
Indicates that an entity is equipped with or includes a reactor coolant system used to remove heat from a reactor.
-
C.
primaryFluidType
chosen
Indicates the main kind of fluid associated with, used by, or flowing through an entity.
-
D.
coolant
Indicates that one entity functions as a coolant for another, serving to absorb and remove heat from it.
-
E.
coolingMethod
Indicates the technique or process used to remove heat from something or keep it at a lower temperature.
- 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_69ef148accd48190b6ed6e13a15f2a4f |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69ff0b6bc4a88190bf1d38c6ea26bcdc |
completed | May 9, 2026, 10:24 a.m. |
| PD | Predicate disambiguation | batch_69ff082a22f4819095ded971dbd8ea7b |
completed | May 9, 2026, 10:10 a.m. |
Created at: April 27, 2026, 8:51 a.m.