Triple
T34713215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Combat de Prairial |
E1000702
|
entity |
| Predicate | FrenchShipsCapturedOrSunk |
P178866
|
FINISHED |
| Object | several ships of the line |
—
|
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: several ships of the line | Statement: [Combat de Prairial, FrenchShipsCapturedOrSunk, several ships of the line]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: FrenchShipsCapturedOrSunk Context triple: [Combat de Prairial, FrenchShipsCapturedOrSunk, several ships of the line]
-
A.
frenchShipsOfTheLineCaptured
chosen
Indicates that the subject is a French ship of the line that was captured, with the object specifying the captor or capturing event.
-
B.
FrenchShips
Indicates that the ships involved are associated with France, typically by nationality, registration, or operation under the French flag.
-
C.
frenchShipOfTheLineSunk
Indicates that a French ship of the line was sunk in an event or engagement.
-
D.
FrenchShipsOfTheLineEngaged
Indicates that French ships of the line were actively involved in a naval engagement or battle.
-
E.
FrenchShipType
Indicates that something is classified as a type of ship associated with France, such as by origin, design, or service.
- 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_69f76dad3f108190a280fd0a2f4ee89a |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f77ffa6b68819090257fed3802c239 |
completed | May 3, 2026, 5:03 p.m. |
| PD | Predicate disambiguation | batch_69f7795978c481909e152cd1bd02dd07 |
completed | May 3, 2026, 4:35 p.m. |
Created at: May 3, 2026, 3:59 p.m.