Triple
T5609736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Marianas Turkey Shoot |
E147321
|
entity |
| Predicate | USAircraftLosses |
P28346
|
FINISHED |
| Object | dozens of aircraft lost |
—
|
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: dozens of aircraft lost | Statement: [Great Marianas Turkey Shoot, USAircraftLosses, dozens of aircraft lost]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: USAircraftLosses Context triple: [Great Marianas Turkey Shoot, USAircraftLosses, dozens of aircraft lost]
-
A.
aircraftLosses
chosen
Indicates the number or occurrence of aircraft that have been destroyed, damaged beyond repair, or otherwise lost.
-
B.
aircraftLostByAllies
Indicates that the specified aircraft was lost by Allied forces (e.g., destroyed, missing, or otherwise no longer operational under their control).
-
C.
aircraftDestroyedUS
Indicates that a U.S. aircraft has been destroyed.
-
D.
aircraftLostByUnitedStates
Indicates that an aircraft was lost (e.g., destroyed, missing, or otherwise no longer operational) and that this loss is attributed to the United States.
-
E.
aircraftAlliedApprox
Indicates that one aircraft is approximately allied with another, reflecting a likely but not definitively confirmed friendly relationship between them.
- 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_69c0090500f881908374285baf0ac46f |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0211dfc88819097b6d4254a61f65a |
completed | March 22, 2026, 5:04 p.m. |
| PD | Predicate disambiguation | batch_69c01b1b3c98819080687d18ab10a914 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:39 p.m.