Triple
T35483461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fiat G.91 |
E1025527
|
entity |
| Predicate | roleOfVariant Fiat G.91T |
P188829
|
FINISHED |
| Object | two-seat trainer |
—
|
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: two-seat trainer | Statement: [Fiat G.91, roleOfVariant Fiat G.91T, two-seat trainer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleOfVariant Fiat G.91T Context triple: [Fiat G.91, roleOfVariant Fiat G.91T, two-seat trainer]
-
A.
roleOfVariant Fiat G.91R
Indicates that the subject is a specific role, configuration, or variant of the Fiat G.91R aircraft.
-
B.
specificCarrierAircraftVariant
Indicates that one aircraft variant is a specific version designed or adapted for carrier-based operations of another, more general aircraft variant.
-
C.
usesAircraftVariant
Indicates that one entity operates or employs a specific variant or version of an aircraft.
-
D.
isMilitaryVariantOf
Indicates that one entity is a military-specific version or adaptation of another, typically civilian or general-purpose, entity.
-
E.
fuselageVariant
Indicates that one fuselage is a specific version or variant derived from another fuselage design.
- F. None of above. chosen
Provenance (4 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_69f76dfbcdd881908c7b0b6bc502252b |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fbad1e94988190b86d447a68e65067 |
completed | May 6, 2026, 9:05 p.m. |
| PD | Predicate disambiguation | batch_69fba881b8e0819094790935152b99a1 |
completed | May 6, 2026, 8:45 p.m. |
| PDg | Predicate description generation | batch_69fbad1b3ba08190ad69e21461333f2e |
completed | May 6, 2026, 9:05 p.m. |
Created at: May 3, 2026, 4:04 p.m.