Triple
T2599424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siege of Wexford |
E58306
|
entity |
| Predicate | hasMilitaryOperationType |
P35485
|
FINISHED |
| Object | siege and assault |
—
|
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: siege and assault | Statement: [Siege of Wexford, hasMilitaryOperationType, siege and assault]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMilitaryOperationType Context triple: [Siege of Wexford, hasMilitaryOperationType, siege and assault]
-
A.
militaryActivity
chosen
Indicates that an entity is engaged in, associated with, or affected by actions, operations, or preparations conducted by armed forces.
-
B.
fieldOfOperation
Indicates the domain, area, or scope within which an entity operates or carries out its primary activities.
-
C.
hasMilitaryPresence
Indicates that a military force is present in, stationed at, or operating within a particular location or entity.
-
D.
hasMilitaryAssociation
Indicates a relationship in which an entity is connected or affiliated with a military organization, activity, or function.
-
E.
eraOfOperation
Indicates the time period or span of years during which an entity was active or in operation.
- 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_69ab4ac14040819098b13f4a27d5c8ff |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd4563b8c8190934616651e93654c |
completed | March 7, 2026, 7:31 a.m. |
| PD | Predicate disambiguation | batch_69abd0d4e8648190b612eb09aa085451 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:49 p.m.