Triple
T29814203
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | bombing of 23 February 1945 |
E757058
|
entity |
| Predicate | percentageOfCityDestroyed |
P136151
|
FINISHED |
| Object | about 80 percent of the city |
—
|
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: about 80 percent of the city | Statement: [bombing of 23 February 1945, percentageOfCityDestroyed, about 80 percent of the city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: percentageOfCityDestroyed Context triple: [bombing of 23 February 1945, percentageOfCityDestroyed, about 80 percent of the city]
-
A.
buildingsDamagedOrDestroyedPercentage
chosen
Indicates the proportion of buildings that have been damaged or completely destroyed relative to the total number of buildings in the relevant area or set.
-
B.
numberOfDistrictsDestroyed
Indicates the quantity of districts that have been destroyed in a given context or event.
-
C.
mainCityDestroyed
Indicates that the primary or central city associated with an entity has been destroyed.
-
D.
destroyedCity
Indicates that an entity has caused the complete or near-complete destruction of a city.
-
E.
buildingsDestroyed
Indicates that one or more buildings have been damaged to the point of destruction as a result of some event or action.
- 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_69f2245701c88190ad42415a0956c4ed |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6953bafb88190a860e9c68a3dd4b2 |
completed | May 3, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69f690ed5d008190831cf8e44cce28af |
completed | May 3, 2026, 12:03 a.m. |
Created at: April 29, 2026, 5:25 p.m.