Triple
T4393215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Operation Rheinübung |
E99414
|
entity |
| Predicate | involvesAircraft |
P2541
|
FINISHED |
| Object | Fairey Swordfish torpedo bombers |
—
|
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: Fairey Swordfish torpedo bombers | Statement: [Operation Rheinübung, involvesAircraft, Fairey Swordfish torpedo bombers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvesAircraft Context triple: [Operation Rheinübung, involvesAircraft, Fairey Swordfish torpedo bombers]
-
A.
aircraft
chosen
Indicates that an entity is an aircraft or functions in the role of an aircraft in the described context.
-
B.
aircraftInvolvedInDeath
Indicates that an aircraft played a direct role in causing or contributing to a person's death.
-
C.
aircraftImpact
Indicates that an aircraft collides with or crashes into a target or surface, causing an impact event.
-
D.
relatedAircraft
Indicates that there is an association or connection between two aircraft, such as operational, functional, or contextual relatedness.
-
E.
airlineInvolved
Indicates that a specific airline is directly associated with, or plays a role in, a particular event, situation, or context.
- 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_69b345506b408190b0e3dee616738a7d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352a8862481909dc67abf42be6928 |
completed | March 12, 2026, 11:56 p.m. |
| PD | Predicate disambiguation | batch_69b34f572efc8190bad1e5078cbcb75a |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:19 p.m.