Triple
T13960029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A View of Delft after the Explosion of 1654 |
E335766
|
entity |
| Predicate | depictsDestruction |
P59907
|
FINISHED |
| Object | urban area |
—
|
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: urban area | Statement: [A View of Delft after the Explosion of 1654, depictsDestruction, urban area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: depictsDestruction Context triple: [A View of Delft after the Explosion of 1654, depictsDestruction, urban area]
-
A.
evidenceOfDestruction
Indicates that there is proof or indication that something has been damaged, ruined, or destroyed.
-
B.
depictsExplosion
chosen
Indicates that one entity visually represents or portrays an explosion involving another entity or within a scene.
-
C.
sufferedDestructionOf
Indicates that one entity experienced damage, ruin, or loss as a result of the destruction of another entity.
-
D.
hasDemolitionOrDestruction
Indicates that one entity causes, undergoes, or is associated with the demolition or destruction of another entity.
-
E.
designedToDestroy
Indicates that one entity was intentionally created or configured for the purpose of damaging, disabling, or annihilating another 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e7b2f908190aa32f22298964746 |
completed | April 14, 2026, 12:09 p.m. |
| PD | Predicate disambiguation | batch_69de05a3ccf88190b45c742db483fa08 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:17 p.m.