Triple
T12272650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal Palace of the Oba of Benin |
E292508
|
entity |
| Predicate | damageOrDestruction |
P32756
|
FINISHED |
| Object | partially destroyed in 1897 |
—
|
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: partially destroyed in 1897 | Statement: [Royal Palace of the Oba of Benin, damageOrDestruction, partially destroyed in 1897]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: damageOrDestruction Context triple: [Royal Palace of the Oba of Benin, damageOrDestruction, partially destroyed in 1897]
-
A.
damageTo
chosen
Indicates a relationship where one entity causes harm, loss, or deterioration to another entity.
-
B.
hasDemolitionOrDestruction
Indicates that one entity causes, undergoes, or is associated with the demolition or destruction of another entity.
-
C.
typeOfDestruction
Indicates the specific manner or method by which something is destroyed or caused to cease to exist.
-
D.
damageLeadsTo
Indicates that one instance of damage causally results in or contributes to another specified outcome or condition.
-
E.
sufferedDestructionOf
Indicates that one entity experienced damage, ruin, or loss as a result of the destruction of 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_69d6ab6856488190b5d31178d5015f8e |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d9380a5e78819086bd4dfe9a83d1f5 |
completed | April 10, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69d91c4a66cc819083ce6fcaf5042af6 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:52 p.m.