Triple
T628695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Operation Telic |
E15876
|
entity |
| Predicate | casualtiesUKWounded |
P4808
|
FINISHED |
| Object | over 5,000 |
—
|
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 5,000 | Statement: [Operation Telic, casualtiesUKWounded, over 5,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesUKWounded Context triple: [Operation Telic, casualtiesUKWounded, over 5,000]
-
A.
casualtiesBritishWounded
chosen
Indicates the number of British individuals who were wounded as a result of a specific event or action.
-
B.
casualtiesWoundedUS
Indicates that the relationship specifies the number of U.S. individuals who were wounded as casualties in an event or incident.
-
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.
casualtiesDescription
Indicates a textual description of the human losses (such as deaths, injuries, or missing persons) resulting from an event or incident.
-
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_69a4935c131c8190a5378c6bf101e8cc |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49e5b5a308190a62165f9275e2f5f |
completed | March 1, 2026, 8:15 p.m. |
| PD | Predicate disambiguation | batch_69a49d01b29081908be87e4cd7726ff1 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:35 p.m.