Triple
T3654061
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LMT |
E77487
|
entity |
| Predicate | associatedWithAerospaceProducts |
P7940
|
FINISHED |
| Object | military aircraft |
—
|
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: military aircraft | Statement: [LMT, associatedWithAerospaceProducts, military aircraft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithAerospaceProducts Context triple: [LMT, associatedWithAerospaceProducts, military aircraft]
-
A.
associatedWithMuseumAircraft
Indicates a relationship where an aircraft is connected to, displayed by, or otherwise part of a museum’s collection or exhibits.
-
B.
avionicsSupplier
Indicates that one entity supplies avionics systems, components, or related services to another entity.
-
C.
spacecraftComponents
Indicates that one entity is a spacecraft and the other is a component or part that belongs to or is used in that spacecraft.
-
D.
relatedAircraft
Indicates that there is an association or connection between two aircraft, such as operational, functional, or contextual relatedness.
-
E.
usedInAviation
chosen
Indicates that something is employed or applied within the field or context of aviation.
- 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_69ad85def5cc8190863dccf55a18bebb |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc3b9164c81908938a4338430d193 |
completed | March 8, 2026, 6:45 p.m. |
| PD | Predicate disambiguation | batch_69adb84650148190bf79231105e58d7f |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:24 p.m.