Triple
T98720
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ryan NYP monoplane |
E1991
|
entity |
| Predicate | aircraftRole |
P2860
|
FINISHED |
| Object | long-range special-purpose 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: long-range special-purpose aircraft | Statement: [Ryan NYP monoplane, aircraftRole, long-range special-purpose aircraft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aircraftRole Context triple: [Ryan NYP monoplane, aircraftRole, long-range special-purpose aircraft]
-
A.
primaryAircraftRole
chosen
Indicates the main operational function or mission type an aircraft is primarily designed or used to perform.
-
B.
aircraftType
Indicates the specific model or category of aircraft associated with an entity or event.
-
C.
aircraft
Indicates that an entity is an aircraft or functions in the role of an aircraft in the described context.
-
D.
aircraftFlown
Indicates that an entity (typically a person or organization) operates or pilots a particular aircraft.
-
E.
aircraftManufacturerUsed
Indicates that a particular aircraft manufacturer was employed or utilized in relation to another entity, such as for production, design, or supply purposes.
- 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_69a24d4862f881908cc8b89d3a78031d |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a24ff07d148190a59aee12c807659d |
completed | Feb. 28, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69a24ebe7b1c8190a6bfbf31dc7c7f07 |
completed | Feb. 28, 2026, 2:11 a.m. |
Created at: Feb. 28, 2026, 2:09 a.m.