Triple
T112162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | F-35B Lightning II |
E2270
|
entity |
| Predicate | airframeConfiguration |
P3541
|
FINISHED |
| Object | single-seat, single-engine, supersonic jet |
—
|
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: single-seat, single-engine, supersonic jet | Statement: [F-35B Lightning II, airframeConfiguration, single-seat, single-engine, supersonic jet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airframeConfiguration Context triple: [F-35B Lightning II, airframeConfiguration, single-seat, single-engine, supersonic jet]
-
A.
aircraftConfiguration
chosen
Indicates the specific arrangement or setup of an aircraft’s components, systems, or features for a given purpose or operating condition.
-
B.
aircraftType
Indicates the specific model or category of aircraft associated with an entity or event.
-
C.
cockpitType
Indicates the specific configuration or style of cockpit associated with an entity (e.g., vehicle or aircraft).
-
D.
aircraft
Indicates that an entity is an aircraft or functions in the role of an aircraft in the described context.
-
E.
landingGearType
Indicates the specific kind or configuration of landing gear that an object (typically an aircraft or vehicle) uses.
- 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_69a24fcdaeb48190a2d796677e4b3281 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NER | Named-entity recognition | batch_69a258808ff08190a06b6206f635612b |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a256425a488190959d71e39e699d90 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:20 a.m.