Triple
T13860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eastern Front (World War II) |
E277
|
entity |
| Predicate | casualtiesEstimate |
P661
|
FINISHED |
| Object | tens of millions of military and civilian deaths |
—
|
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 millions of military and civilian deaths | Statement: [Eastern Front (World War II), casualtiesEstimate, tens of millions of military and civilian deaths]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesEstimate Context triple: [Eastern Front (World War II), casualtiesEstimate, tens of millions of military and civilian deaths]
-
A.
causeOfDeath
Indicates the specific factor, event, or condition that directly resulted in an entity’s death.
-
B.
placeOfDeath
Indicates the location where an entity (typically a person or animal) died.
-
C.
militaryConflict
Indicates a relationship where two or more parties are engaged in organized, armed hostilities or warfare against each other.
-
D.
countryOfDeath
Indicates the country in which an entity (typically a person) died.
-
E.
countryOfBurial
Indicates the country in which a person or entity is buried.
- 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_69a23d7ad88c8190bffe8ab091d86642 |
completed | Feb. 28, 2026, 12:57 a.m. |
| NER | Named-entity recognition | batch_69a240b249788190af8dbf7e80e9c91b |
completed | Feb. 28, 2026, 1:11 a.m. |
| PD | Predicate disambiguation | batch_69a23feae8c481908d8c50faac01fc5c |
completed | Feb. 28, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69a240b1551c81908abcae128ea45d00 |
completed | Feb. 28, 2026, 1:11 a.m. |
Created at: Feb. 28, 2026, 1:02 a.m.