Triple
T7465655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Operation Iraqi Freedom |
E176365
|
entity |
| Predicate | casualtiesIraqiMilitaryAndPolice |
P48077
|
FINISHED |
| Object | tens of thousands killed |
—
|
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: tens of thousands killed | Statement: [Operation Iraqi Freedom, casualtiesIraqiMilitaryAndPolice, tens of thousands killed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesIraqiMilitaryAndPolice Context triple: [Operation Iraqi Freedom, casualtiesIraqiMilitaryAndPolice, tens of thousands killed]
-
A.
casualtiesIraqiMilitaryKilled
chosen
Indicates that the number of casualties refers specifically to Iraqi military personnel who were killed.
-
B.
AfghanCasualties
Indicates the number or occurrence of casualties suffered by Afghan individuals or forces in a given event or context.
-
C.
armedConflictTheatre
Indicates a geographic area or location where an armed conflict takes place or is actively occurring.
-
D.
nativeCasualties
Indicates that native or indigenous people suffered deaths or injuries as a result of a particular event, action, or conflict.
-
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_69c69f21632481908bf83f6c6da897e3 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f3f412908190ac910ecae682d6f6 |
completed | March 27, 2026, 9:17 p.m. |
| PD | Predicate disambiguation | batch_69c6f03bad9c8190bdd5abb86d37df47 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:40 p.m.