Triple
T261842
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | War in Afghanistan (2001–2021) |
E5556
|
entity |
| Predicate | UScasualtiesMilitaryWounded |
P824
|
FINISHED |
| Object | over 20,000 |
—
|
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: over 20,000 | Statement: [War in Afghanistan (2001–2021), UScasualtiesMilitaryWounded, over 20,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: UScasualtiesMilitaryWounded Context triple: [War in Afghanistan (2001–2021), UScasualtiesMilitaryWounded, over 20,000]
-
A.
casualtiesWoundedUS
chosen
Indicates that the relationship specifies the number of U.S. individuals who were wounded as casualties in an event or incident.
-
B.
casualtiesUnitedStates
Indicates that the event or situation resulted in casualties (deaths and/or injuries) among United States personnel or citizens.
-
C.
casualtiesBritishWounded
Indicates the number of British individuals who were wounded as a result of a specific event or action.
-
D.
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.
-
E.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
- 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_69a2580a64ac8190ad76e34bb0715b5e |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25e2aba74819093eddd8d820260c0 |
completed | Feb. 28, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69a25b6c968c819094fc903a3a377e15 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:55 a.m.