Triple
T511110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soviet Air Forces |
E10609
|
entity |
| Predicate | peakAircraftInventory |
P5710
|
FINISHED |
| Object | several tens of thousands of aircraft |
—
|
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: several tens of thousands of aircraft | Statement: [Soviet Air Forces, peakAircraftInventory, several tens of thousands of aircraft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peakAircraftInventory Context triple: [Soviet Air Forces, peakAircraftInventory, several tens of thousands of aircraft]
-
A.
hasAircraftOnDisplay
Indicates that an entity exhibits or presents an aircraft as part of a display or collection.
-
B.
numberOfPlanes
chosen
Indicates the quantity of planes associated with or involved in a given entity or situation.
-
C.
aircraftStrengthPeak
Indicates the maximum strength or capability level that an aircraft reaches during its operational performance.
-
D.
aircraftTypesCarried
Indicates that one entity (typically a vessel, facility, or platform) carries or is capable of carrying specific types of aircraft as part of its operations or configuration.
-
E.
airTraffic
Indicates the movement and flow of aircraft through airspace, including their routes, density, and interactions while in flight.
- 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_69a2e84a0d08819087e01863fcd9abf1 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f165b91c81908c2d2ba15c64b956 |
completed | Feb. 28, 2026, 1:45 p.m. |
| PD | Predicate disambiguation | batch_69a2edfe236481909901cc7d4281b33c |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.