Triple
T6677428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soviet Berlin Offensive Operation |
E151887
|
entity |
| Predicate | symbolicEvent |
P71693
|
FINISHED |
| Object | raising of the Soviet flag over the Reichstag |
—
|
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: raising of the Soviet flag over the Reichstag | Statement: [Soviet Berlin Offensive Operation, symbolicEvent, raising of the Soviet flag over the Reichstag]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: symbolicEvent Context triple: [Soviet Berlin Offensive Operation, symbolicEvent, raising of the Soviet flag over the Reichstag]
-
A.
alternativeEvent
Indicates that one event serves as an alternative or substitute option to another event within the same context.
-
B.
significantEventType
Indicates the specific category or kind of major or noteworthy event associated with an entity or situation.
-
C.
significantEvent
Indicates that an event involving the entities is of notable importance or impact within a given context.
-
D.
typicalEvent
Indicates that the associated event is a common, characteristic, or prototypical occurrence for the given entity or situation.
-
E.
eventName
Indicates the specific label or title assigned to identify an event within a system 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_69c687f830bc81909eb8b04dbb8450b1 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6c0aa8c5c8190a302b261f11b70cb |
completed | March 27, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c6ad0b6d00819086205b8ce30dd045 |
completed | March 27, 2026, 4:15 p.m. |
| PDg | Predicate description generation | batch_69c6c0a90a088190978061cb05dbe268 |
completed | March 27, 2026, 5:38 p.m. |
Created at: March 27, 2026, 2:03 p.m.