Triple
T15730820
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lieutenant General (United States Space Force) |
E381339
|
entity |
| Predicate | typicalBilateralEquivalent |
P119967
|
FINISHED |
| Object | Air Marshal |
—
|
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: Air Marshal | Statement: [Lieutenant General (United States Space Force), typicalBilateralEquivalent, Air Marshal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalBilateralEquivalent Context triple: [Lieutenant General (United States Space Force), typicalBilateralEquivalent, Air Marshal]
-
A.
isBilateral
Indicates that the relationship or interaction involves two sides, parties, or entities mutually.
-
B.
typicalBase
Indicates that one entity serves as the standard or most representative base or foundation for another entity in typical or common cases.
-
C.
typicalIn
Indicates that something commonly occurs, appears, or is found within a given context, category, or environment.
-
D.
bilateralRelation
Indicates a mutual or two-way relationship between two entities, where each affects or interacts with the other.
-
E.
twinType
Indicates that one entity is classified as a specific type or category of twin in relation to another entity.
- 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04fb61cb881908b158609c1ccfa1e |
completed | April 16, 2026, 2:55 a.m. |
| PD | Predicate disambiguation | batch_69e00526759c819088b80d85138b8974 |
completed | April 15, 2026, 9:37 p.m. |
| PDg | Predicate description generation | batch_69e0094af5b481908ad51d5d7ba0c726 |
completed | April 15, 2026, 9:55 p.m. |
Created at: April 10, 2026, 4:46 a.m.