Triple
T13576982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antonov An-12 |
E324310
|
entity |
| Predicate | typicalParatroopCapacity |
P17501
|
FINISHED |
| Object | 40 paratroopers |
—
|
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: 40 paratroopers | Statement: [Antonov An-12, typicalParatroopCapacity, 40 paratroopers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalParatroopCapacity Context triple: [Antonov An-12, typicalParatroopCapacity, 40 paratroopers]
-
A.
paratroopCapacity
chosen
Indicates the maximum number of paratroopers or amount of airborne troops that something (typically a vehicle or vessel) is capable of carrying or deploying.
-
B.
soldiersCapacity
Indicates the maximum number of soldiers that an entity can hold, support, or accommodate.
-
C.
designedCargoCapacity
Indicates the maximum amount of cargo an object (such as a vehicle or container) was originally engineered or specified to carry.
-
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.
militaryCargoOnBoard
Indicates that a vehicle, vessel, or aircraft is currently carrying military-related cargo on board.
- 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_69d80769100c819099111274614f5ed2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb02de1988190af2d473973ecd529 |
completed | April 12, 2026, 2:46 p.m. |
| PD | Predicate disambiguation | batch_69dbae161a0481909f9d3f40ca4e0ac5 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:48 p.m.