Triple
T12770401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vimy Ridge |
E305232
|
entity |
| Predicate | battleCasualtiesGerman |
P14574
|
FINISHED |
| Object | thousands of German casualties |
—
|
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: thousands of German casualties | Statement: [Vimy Ridge, battleCasualtiesGerman, thousands of German casualties]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: battleCasualtiesGerman Context triple: [Vimy Ridge, battleCasualtiesGerman, thousands of German casualties]
-
A.
GermanLoss
Indicates that Germany experiences a loss, defeat, or negative outcome in the specified context or event.
-
B.
numberOfGermanVictims
chosen
Indicates the quantity of victims who are identified as German in the context of the described event or situation.
-
C.
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.
-
D.
casualtiesFrancoBavarian
Indicates that there were casualties suffered by the Franco-Bavarian side in a particular conflict or event.
-
E.
militaryCasualtiesSide
Indicates the side or party in a conflict to which the recorded military casualties belong.
- 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_69d7bdf2b43c819098ae5aa68e61ea58 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96df4b36c81909bcc913dd5e535f8 |
completed | April 10, 2026, 9:39 p.m. |
| PD | Predicate disambiguation | batch_69d96409739881909174ba005a986cb5 |
completed | April 10, 2026, 8:56 p.m. |
Created at: April 9, 2026, 5:28 p.m.