Triple
T4741159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Presidential Office Building |
E105243
|
entity |
| Predicate | damagedInEvent |
P992
|
FINISHED |
| Object | World War II air raids |
—
|
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 air raids | Statement: [Presidential Office Building, damagedInEvent, World War II air raids]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: damagedInEvent Context triple: [Presidential Office Building, damagedInEvent, World War II air raids]
-
A.
damagedIn
chosen
Indicates that an entity has suffered harm, impairment, or destruction as a result of a specified event, process, or condition.
-
B.
damagedBy
Indicates that one entity has caused harm, impairment, or deterioration to another entity.
-
C.
damageTo
Indicates a relationship where one entity causes harm, loss, or deterioration to another entity.
-
D.
hasDam
Indicates that a watercourse, reservoir, or similar feature is impounded or controlled by a specific dam.
-
E.
damageYear
Indicates the year in which the damage to an entity occurred or was recorded.
- 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_69bd43ef87a48190a5bc3600711aa032 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd64a5f3548190a6acf1dcfd64d11d |
completed | March 20, 2026, 3:15 p.m. |
| PD | Predicate disambiguation | batch_69bd6221c3b881908604f35f8de6f16b |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:19 p.m.