Triple
T21433509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nazi book burning of 10 May 1933 |
E528747
|
entity |
| Predicate | estimatedNumberOfBooksDestroyed |
P143940
|
FINISHED |
| Object | tens of thousands |
—
|
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: tens of thousands | Statement: [Nazi book burning of 10 May 1933, estimatedNumberOfBooksDestroyed, tens of thousands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: estimatedNumberOfBooksDestroyed Context triple: [Nazi book burning of 10 May 1933, estimatedNumberOfBooksDestroyed, tens of thousands]
-
A.
lostBooks
Indicates that certain books are missing, misplaced, or no longer in the possession of their expected owner or location.
-
B.
intendedNumberOfBooks
Indicates the number of books that an agent plans or aims to have, produce, read, or otherwise be associated with, as opposed to the number actually realized.
-
C.
estimatedTeaChestsDestroyed
Indicates the estimated number of tea chests that were destroyed in a given event or context.
-
D.
bookBurningTool
Indicates that an entity is used as a tool or instrument for burning books.
-
E.
numberOfBusinessesDestroyed
Indicates the quantity of businesses that have been destroyed in a given event or context.
- F. None of above. chosen
Provenance (4 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_69e0c4569fa081908101baa24f8745db |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b5344c048190adfa6f6dd1b453ac |
completed | April 22, 2026, 11:47 a.m. |
| PD | Predicate disambiguation | batch_69e61639ee288190889ffd500d1260f6 |
completed | April 20, 2026, 12:04 p.m. |
| PDg | Predicate description generation | batch_69e6190163448190a2404b396215c686 |
completed | April 20, 2026, 12:16 p.m. |
Created at: April 16, 2026, 6 p.m.