Triple
T581985
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eurofighter Typhoon Tranche 2 |
E15077
|
entity |
| Predicate | hasCockpit |
P3544
|
FINISHED |
| Object | glass cockpit |
—
|
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: glass cockpit | Statement: [Eurofighter Typhoon Tranche 2, hasCockpit, glass cockpit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCockpit Context triple: [Eurofighter Typhoon Tranche 2, hasCockpit, glass cockpit]
-
A.
cockpitType
chosen
Indicates the specific configuration or style of cockpit associated with an entity (e.g., vehicle or aircraft).
-
B.
hasCabinet
Indicates that one entity possesses, includes, or is equipped with a cabinet associated with it.
-
C.
hasCP
Indicates that an entity possesses, is associated with, or is characterized by a specific CP (such as a control point, contact person, or configuration parameter), depending on the domain context.
-
D.
hasControlTower
Indicates that one entity possesses, hosts, or is equipped with a control tower that manages or oversees its operations.
-
E.
hasPortico
Indicates that one entity (typically a building or structure) features a portico as part of its architectural design.
- 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_69a4935783b8819082b77726ec10cc42 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49b84899881909d5b2b4e67e22d9b |
completed | March 1, 2026, 8:03 p.m. |
| PD | Predicate disambiguation | batch_69a494c7f9008190bd8d05b4dc2a7c7f |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:33 p.m.