Triple
T5999985
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Young Plan |
E133568
|
entity |
| Predicate | totalReparationsAmount |
P19678
|
FINISHED |
| Object | approximately 112 billion Reichsmarks |
—
|
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: approximately 112 billion Reichsmarks | Statement: [Young Plan, totalReparationsAmount, approximately 112 billion Reichsmarks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalReparationsAmount Context triple: [Young Plan, totalReparationsAmount, approximately 112 billion Reichsmarks]
-
A.
reparationAmountPerPerson
Indicates the specific amount of reparations allocated or owed to each individual person involved.
-
B.
reparationsType
Indicates the specific category or form of reparations involved in a reparative action or obligation between entities.
-
C.
warIndemnityAmount
chosen
Indicates the amount of financial compensation required or paid as indemnity as a result of a war or armed conflict.
-
D.
totalAidAmountAdjusted
Indicates the total amount of aid provided, after applying any relevant adjustments such as corrections, discounts, or recalculations.
-
E.
totalAidAmountUSD
Indicates the total monetary value of aid provided, expressed in U.S. dollars.
- 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_69c00870ddbc81909880fa3864f4f38d |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04ee5e7bc8190aaa87605fa7b102e |
completed | March 22, 2026, 8:19 p.m. |
| PD | Predicate disambiguation | batch_69c049e152e88190979ab80cb9b50321 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:05 p.m.