Triple
T4937710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joan Blaeu |
E110851
|
entity |
| Predicate | printingOfficeDestroyedBy |
P5325
|
FINISHED |
| Object | fire of 1672 in Amsterdam |
—
|
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: fire of 1672 in Amsterdam | Statement: [Joan Blaeu, printingOfficeDestroyedBy, fire of 1672 in Amsterdam]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: printingOfficeDestroyedBy Context triple: [Joan Blaeu, printingOfficeDestroyedBy, fire of 1672 in Amsterdam]
-
A.
sufferedDestructionOf
Indicates that one entity experienced damage, ruin, or loss as a result of the destruction of another entity.
-
B.
locationOfDestruction
Indicates the place where a destruction event occurred or where something was destroyed.
-
C.
hasCauseOfDestruction
chosen
Indicates that one entity is the cause or agent responsible for the destruction or damage of another entity.
-
D.
destroyedDuring
Indicates that one entity was destroyed in the course of, or as a consequence of, a specified event or time period.
-
E.
sufferedDestructionIn
Indicates that an entity experienced damage, ruin, or devastation during or as part of a specified event or period.
- 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_69bd4415eee08190bdce70276e56a5b4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd70872270819080769dad972681ef |
completed | March 20, 2026, 4:06 p.m. |
| PD | Predicate disambiguation | batch_69bd6c389b9881908ad7fb1c5393c1b1 |
completed | March 20, 2026, 3:48 p.m. |
Created at: March 20, 2026, 1:31 p.m.