Triple
T17134972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Mers-el-Kébir |
E415812
|
entity |
| Predicate | shipDamage |
P822
|
FINISHED |
| Object | French battleship Dunkerque heavily damaged |
—
|
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 Dunkerque heavily damaged | Statement: [Battle of Mers-el-Kébir, shipDamage, French battleship Dunkerque heavily damaged]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: shipDamage Context triple: [Battle of Mers-el-Kébir, shipDamage, French battleship Dunkerque heavily damaged]
-
A.
battleshipsDamaged
chosen
Indicates that one or more battleships have sustained damage, typically as a result of combat or hostile action.
-
B.
shipAttacked
Indicates that one ship has carried out an attack against another ship.
-
C.
fleetDestroyedBy
Indicates that a fleet was destroyed as a direct result of actions taken by another specified entity.
-
D.
shipwrecksDestroyedIn
Indicates that one or more shipwrecks were destroyed within a specified location or during a particular event or time period.
-
E.
shipTypeSunk
Indicates that a particular type of ship has been sunk as a result of some event or action.
- 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.