Triple
T17134971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Mers-el-Kébir |
E415812
|
entity |
| Predicate | shipLoss |
P821
|
FINISHED |
| Object | French battleship Bretagne sunk |
—
|
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: French battleship Bretagne sunk | Statement: [Battle of Mers-el-Kébir, shipLoss, French battleship Bretagne sunk]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: shipLoss Context triple: [Battle of Mers-el-Kébir, shipLoss, French battleship Bretagne sunk]
-
A.
shipsSunkOrTotalLoss
chosen
Indicates that the referenced ships were sunk or otherwise rendered a total loss (permanently unusable).
-
B.
shipwrecksDestroyedIn
Indicates that one or more shipwrecks were destroyed within a specified location or during a particular event or time period.
-
C.
lostToSea
Indicates that something has been carried away or disappeared into the sea, resulting in its loss.
-
D.
shipTypeSunk
Indicates that a particular type of ship has been sunk as a result of some event or action.
-
E.
fleetDestroyedBy
Indicates that a fleet was destroyed as a direct result of actions taken by another specified entity.
- 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_69d886d15af4819092f92f8a129763e6 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f02dca8881908efd73741397a207 |
completed | April 18, 2026, 8:57 p.m. |
| PD | Predicate disambiguation | batch_69e3830192ac819091344a9e5a36c8c9 |
completed | April 18, 2026, 1:11 p.m. |
Created at: April 10, 2026, 5:36 a.m.