Triple
T14713693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French Flanders campaign of 1745 |
E345619
|
entity |
| Predicate | involvedSiegeWarfare |
P101059
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [French Flanders campaign of 1745, involvedSiegeWarfare, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvedSiegeWarfare Context triple: [French Flanders campaign of 1745, involvedSiegeWarfare, true]
-
A.
siegeRelated
chosen
Indicates a relationship in which one entity is involved with, associated with, or relevant to a siege or siege-related activities.
-
B.
typeOfSiege
Indicates the specific kind or category of siege involved in a conflict or military operation.
-
C.
isSiegeOf
Indicates a relationship where one event or action constitutes the military siege of a particular place, target, or entity.
-
D.
sieged
Indicates that one entity has surrounded and blockaded another entity or location, typically to cut off supplies and force surrender.
-
E.
siegeOccurredIn
Indicates that a siege took place within or at the location specified by the related entity.
- 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_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb98513b081908b230f6ac79c72ad |
completed | April 14, 2026, 10:02 p.m. |
| PD | Predicate disambiguation | batch_69de657c57ec8190ae0b9bb79a514566 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:29 a.m.