Triple
T1549733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal Rifles of Canada |
E33060
|
entity |
| Predicate | notableLosses |
P1706
|
FINISHED |
| Object | heavy casualties and prisoners of war in Hong Kong |
—
|
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 casualties and prisoners of war in Hong Kong | Statement: [Royal Rifles of Canada, notableLosses, heavy casualties and prisoners of war in Hong Kong]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableLosses Context triple: [Royal Rifles of Canada, notableLosses, heavy casualties and prisoners of war in Hong Kong]
-
A.
careerLosses
Indicates the total number of defeats or losses an entity has accumulated over the course of its entire career.
-
B.
gamesLostBy
Indicates the number of games that one entity has been defeated in by another entity.
-
C.
notableOutcome
chosen
Indicates that an action, event, or entity leads to or is associated with a significant, noteworthy result or consequence.
-
D.
losses
Indicates that an entity experiences a decrease in value, quantity, or advantage as a result of some event or comparison.
-
E.
notablePlay
Indicates that a particular play is especially famous, significant, or noteworthy in relation to the entity it is associated with.
- 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_69a885ee6db8819099502bc5ce8af881 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa574094048190a2d7fc3ac904d51e |
completed | March 6, 2026, 4:25 a.m. |
| PD | Predicate disambiguation | batch_69a907b426dc8190975c024a50955368 |
completed | March 5, 2026, 4:33 a.m. |
Created at: March 4, 2026, 7:26 p.m.