Triple
T15325490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Acre Prison break |
E366397
|
entity |
| Predicate | casualtiesIrgunWounded |
P118119
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Acre Prison break, casualtiesIrgunWounded, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesIrgunWounded Context triple: [Acre Prison break, casualtiesIrgunWounded, 5]
-
A.
numberOfIsraeliSoldiersWounded
Indicates the quantity of Israeli soldiers who have been injured or wounded in a given event or context.
-
B.
wasWoundedIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or conflict.
-
C.
UnionKilledAndWounded
Indicates that members of the Union side in a conflict caused deaths and injuries to others.
-
D.
woundedAt
Indicates that an entity was injured or harmed at a specific place or during a particular event.
-
E.
casualtiesBritishWounded
Indicates the number of British individuals who were wounded as a result of a specific event or action.
- F. None of above. chosen
Provenance (4 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_69d85a121520819093dcce999fdefe1a |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03dfd8f048190831b463a2728eafe |
completed | April 16, 2026, 1:40 a.m. |
| PD | Predicate disambiguation | batch_69deca9659f48190b8661df223ce5078 |
completed | April 14, 2026, 11:15 p.m. |
| PDg | Predicate description generation | batch_69decf2e413481909d9180a8d78d2c17 |
completed | April 14, 2026, 11:35 p.m. |
Created at: April 10, 2026, 3:16 a.m.