Triple
T660506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dassault Rafale |
E11742
|
entity |
| Predicate | crewVariant |
P883
|
FINISHED |
| Object | 2 (tandem) for trainer and strike variants |
—
|
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: 2 (tandem) for trainer and strike variants | Statement: [Dassault Rafale, crewVariant, 2 (tandem) for trainer and strike variants]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: crewVariant Context triple: [Dassault Rafale, crewVariant, 2 (tandem) for trainer and strike variants]
-
A.
crewVariantName
Indicates the specific name or label assigned to a particular variant or version of a crew configuration.
-
B.
crew
Indicates that one entity serves as the group of people who operate, staff, or work on another entity (such as a vehicle, vessel, or production).
-
C.
intendedCrewVehicle
Indicates that a particular vehicle is designated or planned to be used by a specific crew.
-
D.
crewModule
Indicates that an object functions as or contains a crew module, i.e., a section designed to house and support crew members.
-
E.
crewCountApproximate
chosen
Indicates that the relationship specifies an estimated or approximate number of crew members associated with an entity.
- 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_69a4932862a0819098be659c814e4981 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a0f55f7481909e052a25bd12d455 |
completed | March 1, 2026, 8:26 p.m. |
| PD | Predicate disambiguation | batch_69a49d1406ec8190abf546549264c85d |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.