Triple
T3397489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German federal election, July 1932 |
E71563
|
entity |
| Predicate | campaignCharacteristics |
P49417
|
FINISHED |
| Object | intense street violence |
—
|
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: intense street violence | Statement: [German federal election, July 1932, campaignCharacteristics, intense street violence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: campaignCharacteristics Context triple: [German federal election, July 1932, campaignCharacteristics, intense street violence]
-
A.
campaignSymbol
Indicates that something serves as a symbol or emblem representing a particular campaign.
-
B.
brandCharacter
Indicates that one entity serves as a brand character or mascot representing another entity (typically a brand or product).
-
C.
campaignOrService
Indicates that one entity is a campaign and the other is a service that the campaign promotes, uses, or is associated with.
-
D.
commercialCharacter
Indicates that an entity is a fictional or stylized persona used in a commercial or advertising context.
-
E.
policyCharacteristic
Indicates that a policy possesses a particular attribute, feature, or quality that characterizes how it is defined or operates.
- 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_69ad85a9c4a88190a854019341cb3b60 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb8c4099081908a236376b4f86900 |
completed | March 8, 2026, 5:58 p.m. |
| PD | Predicate disambiguation | batch_69adadf705608190975423779430cc58 |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adb2e426b88190b82d9830149b142e |
completed | March 8, 2026, 5:33 p.m. |
Created at: March 8, 2026, 3:14 p.m.