Triple
T1643516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clarence House |
E35524
|
entity |
| Predicate | warUse |
P23907
|
FINISHED |
| Object | offices for the Red Cross and St John Ambulance Brigade |
—
|
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: offices for the Red Cross and St John Ambulance Brigade | Statement: [Clarence House, warUse, offices for the Red Cross and St John Ambulance Brigade]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: warUse Context triple: [Clarence House, warUse, offices for the Red Cross and St John Ambulance Brigade]
-
A.
militaryForceUsed
Indicates that one entity employs or applies military power or armed force against, within, or in relation to another entity or situation.
-
B.
militaryUse
chosen
Indicates the use of something (such as land, facilities, equipment, or resources) for military purposes or operations.
-
C.
warfareType
Indicates the specific kind or category of warfare that characterizes a given conflict or military engagement.
-
D.
usedWarfareType
Indicates the specific type or method of warfare that an entity employed in a conflict or military context.
-
E.
warDamage
Indicates damage that was caused as a direct consequence of war or armed conflict.
- 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_69a88604618c81908b41f6429c431eb6 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a919306fd48190a245fc95e0e759d9 |
completed | March 5, 2026, 5:48 a.m. |
| PD | Predicate disambiguation | batch_69a907cc9d348190b76b0d3f596e5a81 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.