Triple
T3599226
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baedeker Blitz |
E76213
|
entity |
| Predicate | materialDamage |
P992
|
FINISHED |
| Object | extensive destruction of historic buildings |
—
|
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: extensive destruction of historic buildings | Statement: [Baedeker Blitz, materialDamage, extensive destruction of historic buildings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: materialDamage Context triple: [Baedeker Blitz, materialDamage, extensive destruction of historic buildings]
-
A.
damageTo
Indicates a relationship where one entity causes harm, loss, or deterioration to another entity.
-
B.
damagedIn
chosen
Indicates that an entity has suffered harm, impairment, or destruction as a result of a specified event, process, or condition.
-
C.
damagedBy
Indicates that one entity has caused harm, impairment, or deterioration to another entity.
-
D.
damageYear
Indicates the year in which the damage to an entity occurred or was recorded.
-
E.
material
Indicates that one entity is physically composed of, made from, or constructed using the substance or material represented by the other entity.
- 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_69ad85d93dcc819094fba90cf70f4996 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc19e9e98819094455cb3c4efcb9a |
completed | March 8, 2026, 6:36 p.m. |
| PD | Predicate disambiguation | batch_69adb83b66708190bb9d2f23d6fd308e |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:22 p.m.