Triple
T22971234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beagle |
E571190
|
entity |
| Predicate | subjectAircraftFirstGenerationJetBomber |
P150448
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Beagle, subjectAircraftFirstGenerationJetBomber, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectAircraftFirstGenerationJetBomber Context triple: [Beagle, subjectAircraftFirstGenerationJetBomber, true]
-
A.
mainBomberAircraft
Indicates that an entity serves as the primary bomber aircraft used by another entity (such as a military force, operation, or country).
-
B.
primaryGermanBomberAircraft
Indicates that the subject is the main type of bomber aircraft used by Germany in a given context or period.
-
C.
firstAircraftName
Indicates the name assigned to the first aircraft associated with a given entity or context.
-
D.
aircraftGeneration
Indicates a generational relationship between aircraft, such as one model being a successor, predecessor, or belonging to a specific generation relative to another.
-
E.
formerAircraft
Indicates that an entity was previously used or designated as an aircraft but no longer holds that status.
- 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_69e245b2c6548190a0e4c7f2f7df2d48 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1823370fc819084a13d6e4eb6e44e |
completed | April 29, 2026, 3:59 a.m. |
| PD | Predicate disambiguation | batch_69ef3b9101f48190a06c69dff26c6441 |
completed | April 27, 2026, 10:33 a.m. |
| PDg | Predicate description generation | batch_69ef538a115081908982597f79355840 |
completed | April 27, 2026, 12:16 p.m. |
Created at: April 17, 2026, 3:48 p.m.