Triple
T24995288
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Myeongnyang |
E625550
|
entity |
| Predicate | casualtiesJoseon |
P157898
|
FINISHED |
| Object | light compared to Japanese losses |
—
|
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: light compared to Japanese losses | Statement: [Battle of Myeongnyang, casualtiesJoseon, light compared to Japanese losses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesJoseon Context triple: [Battle of Myeongnyang, casualtiesJoseon, light compared to Japanese losses]
-
A.
KoreanCasualties
Indicates that an entity experienced casualties (deaths or injuries) in the context of the Korean War.
-
B.
casualtiesJapan
Indicates that an event or action resulted in casualties (deaths and/or injuries) occurring in Japan.
-
C.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
D.
nativeCasualties
Indicates that native or indigenous people suffered deaths or injuries as a result of a particular event, action, or conflict.
-
E.
casualtiesJapanKilled
Indicates that the casualties were individuals from Japan who were killed.
- 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_69e2ff2611c081908710457fbe6d376b |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f453035f508190be83a3d521723acf |
completed | May 1, 2026, 7:15 a.m. |
| PD | Predicate disambiguation | batch_69f44d77f6e88190a4643ab2cbef567b |
completed | May 1, 2026, 6:51 a.m. |
| PDg | Predicate description generation | batch_69f45300bd488190bb1d4160f5534ef6 |
completed | May 1, 2026, 7:15 a.m. |
Created at: April 18, 2026, 6:04 a.m.