Triple
T3976962
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sam Manekshaw |
E85665
|
entity |
| Predicate | woundsDescription |
P28646
|
FINISHED |
| Object | seriously wounded by machine-gun fire in the Burma Campaign |
—
|
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: seriously wounded by machine-gun fire in the Burma Campaign | Statement: [Sam Manekshaw, woundsDescription, seriously wounded by machine-gun fire in the Burma Campaign]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: woundsDescription Context triple: [Sam Manekshaw, woundsDescription, seriously wounded by machine-gun fire in the Burma Campaign]
-
A.
wasWoundedIn
chosen
Indicates that an entity sustained an injury as a result of a specified event, situation, or conflict.
-
B.
injuriesApprox
Indicates an approximate or estimated number or extent of injuries associated with an event or entity.
-
C.
injuryType
Indicates the specific kind or category of injury associated with an entity or event.
-
D.
damageTo
Indicates a relationship where one entity causes harm, loss, or deterioration to another entity.
-
E.
causeOfInjury
Indicates that one entity is the source or reason that another entity sustained an injury.
- 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_69aed93908348190a26c8aaf4fab3e86 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaca33e4819091957c7915857a42 |
completed | March 9, 2026, 4:52 p.m. |
| PD | Predicate disambiguation | batch_69aef8f252b081909749d40440d372b2 |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:33 p.m.