Triple
T2417174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Seonghwan |
E52331
|
entity |
| Predicate | JapaneseCasualtiesWounded |
P37796
|
FINISHED |
| Object | 54 |
—
|
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: 54 | Statement: [Battle of Seonghwan, JapaneseCasualtiesWounded, 54]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: JapaneseCasualtiesWounded Context triple: [Battle of Seonghwan, JapaneseCasualtiesWounded, 54]
-
A.
casualtiesJapan
Indicates that an event or action resulted in casualties (deaths and/or injuries) occurring in Japan.
-
B.
casualtiesGermanWounded
Indicates that the relationship specifies the number of German individuals who were wounded (but not killed) as casualties in a particular event or context.
-
C.
involvedShipJapan
Indicates that a ship associated with Japan was involved in the referenced event or activity.
-
D.
wasWoundedIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or conflict.
-
E.
casualtiesBritishWounded
Indicates the number of British individuals who were wounded as a result of a specific event or action.
- F. None of above. chosen
Provenance (4 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_69ab495622948190bc6bc6e4cddaf645 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abc94eafd481909eeff689e5bf5960 |
completed | March 7, 2026, 6:44 a.m. |
| PD | Predicate disambiguation | batch_69abc5a6cbd0819086c0716e266b7ebb |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abc6011e348190b6f9c038c7559289 |
completed | March 7, 2026, 6:30 a.m. |
Created at: March 6, 2026, 9:42 p.m.