Triple
T23179229
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frauenburg |
E579105
|
entity |
| Predicate | warDamageEvent |
P16536
|
FINISHED |
| Object | World War II destruction |
—
|
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: World War II destruction | Statement: [Frauenburg, warDamageEvent, World War II destruction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: warDamageEvent Context triple: [Frauenburg, warDamageEvent, World War II destruction]
-
A.
warDamage
chosen
Indicates damage that was caused as a direct consequence of war or armed conflict.
-
B.
warTimeEvent
Indicates an event that occurs during a period of war or armed conflict, typically as part of or directly influenced by that conflict.
-
C.
warDamagePeriod
Indicates the time span during which damage caused by war or armed conflict occurred or was in effect.
-
D.
areaDestroyed
Indicates that a specified portion or region has been damaged or ruined to the point of destruction.
-
E.
sustainedHeavyCasualtiesAt
Indicates that an entity experienced a large number of serious losses (e.g., deaths or injuries) at a specific location or during a specific event.
- 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_69e245fd2a388190b814c0dfa15f7148 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18f6dad948190a64f80f2c9e8e4cb |
completed | April 29, 2026, 4:56 a.m. |
| PD | Predicate disambiguation | batch_69ef8a041c0081909afb670d17a5aaba |
completed | April 27, 2026, 4:08 p.m. |
Created at: April 17, 2026, 4:04 p.m.