Triple
T7022067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South African 2nd Division |
E162849
|
entity |
| Predicate | casualtiesAtTobruk |
P75309
|
FINISHED |
| Object | heavy 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: heavy losses | Statement: [South African 2nd Division, casualtiesAtTobruk, heavy losses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesAtTobruk Context triple: [South African 2nd Division, casualtiesAtTobruk, heavy losses]
-
A.
casualtiesAtTannenberg
Indicates that the entities are related through casualties that occurred during the Battle of Tannenberg.
-
B.
casualtiesAtStalingrad
Indicates that an entity experienced casualties (killed, wounded, or missing) in connection with the Battle of Stalingrad.
-
C.
casualtiesDescription
Indicates a textual description of the human losses (such as deaths, injuries, or missing persons) resulting from an event or incident.
-
D.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
E.
casualtiesTotal
Indicates the total number of people killed and injured as a result of a particular event or incident.
- 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_69c6885b26248190a857541e3d10e299 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e5ecd4488190bf19e42de55da98b |
completed | March 27, 2026, 8:17 p.m. |
| PD | Predicate disambiguation | batch_69c6e1b8118481909d76eb6616160e80 |
completed | March 27, 2026, 7:59 p.m. |
| PDg | Predicate description generation | batch_69c6e5eb904481909a900e2ba9df710b |
completed | March 27, 2026, 8:17 p.m. |
Created at: March 27, 2026, 2:35 p.m.