Triple
T1833024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Manila (1945) |
E41000
|
entity |
| Predicate | destructionLevel |
P18719
|
FINISHED |
| Object | large parts of Manila destroyed |
—
|
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: large parts of Manila destroyed | Statement: [Battle of Manila (1945), destructionLevel, large parts of Manila destroyed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: destructionLevel Context triple: [Battle of Manila (1945), destructionLevel, large parts of Manila destroyed]
-
A.
designedToDestroy
Indicates that one entity was intentionally created or configured for the purpose of damaging, disabling, or annihilating another entity.
-
B.
hasCauseOfDestruction
Indicates that one entity is the cause or agent responsible for the destruction or damage of another entity.
-
C.
areaDestroyed
chosen
Indicates that a specified portion or region has been damaged or ruined to the point of destruction.
-
D.
sufferedDestructionIn
Indicates that an entity experienced damage, ruin, or devastation during or as part of a specified event or period.
-
E.
destroyedDuring
Indicates that one entity was destroyed in the course of, or as a consequence of, a specified event or time period.
- 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_69a88647f9388190909bc36e795bdaec |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb32d35508190bf1c487dffbecaf0 |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abafd88ebc81908208394746351fe6 |
completed | March 7, 2026, 4:55 a.m. |
Created at: March 4, 2026, 7:33 p.m.