Triple
T9348112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pratt & Whitney PW1500G |
E224943
|
entity |
| Predicate | coolingFeature |
P11564
|
FINISHED |
| Object | advanced turbine cooling |
—
|
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: advanced turbine cooling | Statement: [Pratt & Whitney PW1500G, coolingFeature, advanced turbine cooling]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coolingFeature Context triple: [Pratt & Whitney PW1500G, coolingFeature, advanced turbine cooling]
-
A.
coolingRequirement
Indicates that an entity requires or is subject to a specific amount or type of cooling to operate within acceptable conditions.
-
B.
coolingMethod
chosen
Indicates the technique or process used to remove heat from something or keep it at a lower temperature.
-
C.
hasCoolingFlow
Indicates that an entity is equipped with or utilizes a system or mechanism to remove heat or maintain a lower temperature through the flow of a cooling medium.
-
D.
hasCoolingSource
Indicates that one entity provides or serves as a cooling source for another entity.
-
E.
coolingStrategy
Indicates the method or approach used to reduce or control temperature in a system or environment.
- 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_69ca842993248190a79ab06968994b86 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd4f107f0081908938f4b814eca5fc |
completed | April 1, 2026, 5 p.m. |
| PD | Predicate disambiguation | batch_69cc7a68ab9481909f97cb70764697cc |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:41 p.m.