Triple
T777736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Treaty of Paris (1815) |
E16426
|
entity |
| Predicate | warIndemnityAmount |
P19678
|
FINISHED |
| Object | 700,000,000 francs |
—
|
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: 700,000,000 francs | Statement: [Treaty of Paris (1815), warIndemnityAmount, 700,000,000 francs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: warIndemnityAmount Context triple: [Treaty of Paris (1815), warIndemnityAmount, 700,000,000 francs]
-
A.
warDamage
Indicates damage that was caused as a direct consequence of war or armed conflict.
-
B.
militaryCasualtiesEstimate
Indicates an estimated number of people killed, wounded, or missing as a result of military conflict or operations.
-
C.
armisticeSignedWith
Indicates that a formal agreement to stop fighting (an armistice) has been concluded between the two related parties.
-
D.
economicDamage
Indicates that one entity causes or experiences financial loss, harm, or negative economic impact as a result of another entity or event.
-
E.
compensatedAustriaForLossOf
Indicates that an entity provided compensation to Austria for a specific loss or damage.
- 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_69a4936ad1fc81908f190208059ccf78 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a74da7648190adfad56717d564df |
completed | March 1, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69a4a50a443481909ae3662764ee69a4 |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a74c81bc81909f4ac9c1677b09c2 |
completed | March 1, 2026, 8:53 p.m. |
Created at: March 1, 2026, 7:37 p.m.