Triple
T14999181
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AF 8x-xxxx |
E374037
|
entity |
| Predicate | appliesToFleet |
P36948
|
FINISHED |
| Object | entire B-2 Spirit production fleet |
—
|
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: entire B-2 Spirit production fleet | Statement: [AF 8x-xxxx, appliesToFleet, entire B-2 Spirit production fleet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliesToFleet Context triple: [AF 8x-xxxx, appliesToFleet, entire B-2 Spirit production fleet]
-
A.
supportedFleet
Indicates that one entity provided assistance, resources, or services to sustain or enable the operations of a fleet.
-
B.
usesFleetOf
Indicates that one entity operates or relies on a group of vehicles, vessels, or similar assets collectively as a fleet to perform its activities or services.
-
C.
targetFleetType
chosen
Indicates the specific category or classification of fleet that is the intended focus or recipient of an action or relationship.
-
D.
appliesTo
Indicates that something is relevant, valid, or has effect in relation to a particular entity, case, or context.
-
E.
appliedToVehicleType
Indicates that something (such as a rule, restriction, or condition) is specifically applicable to a particular type or category of vehicle.
- 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_69d85ccc84388190aa151e5173370c8d |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded71a5618819083ae96a79735ef98 |
completed | April 15, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69de9a6169b48190a679609febd2d0e3 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:54 a.m.