Triple
T24995280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Myeongnyang |
E625550
|
entity |
| Predicate | JapaneseShips |
P681
|
FINISHED |
| Object | over 100 warships |
—
|
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: over 100 warships | Statement: [Battle of Myeongnyang, JapaneseShips, over 100 warships]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: JapaneseShips Context triple: [Battle of Myeongnyang, JapaneseShips, over 100 warships]
-
A.
involvedShipJapan
chosen
Indicates that a ship associated with Japan was involved in the referenced event or activity.
-
B.
GermanShipFate
Indicates the ultimate outcome or disposition of a German ship, such as whether it was sunk, captured, scuttled, or otherwise ended its service.
-
C.
JapaneseDestroyersDamaged
Indicates that one or more Japanese destroyer-class ships have sustained damage.
-
D.
notableVictimShip
Indicates that a ship is recognized as a particularly significant or noteworthy victim in a specific incident or context.
-
E.
reasonForSinking
Indicates the cause or circumstance that led to something sinking.
- 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_69e2ff2611c081908710457fbe6d376b |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f48b9b687881908fd87a2f5fa0b1e7 |
completed | May 1, 2026, 11:16 a.m. |
| PD | Predicate disambiguation | batch_69f48060597c8190a4414e4e4fcb1fec |
completed | May 1, 2026, 10:28 a.m. |
Created at: April 18, 2026, 6:04 a.m.