Triple
T5475081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bombardment of Yeonpyeong |
E122931
|
entity |
| Predicate | civilianInfrastructureDamaged |
P54661
|
FINISHED |
| Object | residential houses |
—
|
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: residential houses | Statement: [Bombardment of Yeonpyeong, civilianInfrastructureDamaged, residential houses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: civilianInfrastructureDamaged Context triple: [Bombardment of Yeonpyeong, civilianInfrastructureDamaged, residential houses]
-
A.
infrastructureDamage
chosen
Indicates damage or destruction affecting physical infrastructure such as buildings, roads, utilities, or other constructed facilities.
-
B.
buildingsDestroyed
Indicates that one or more buildings have been damaged to the point of destruction as a result of some event or action.
-
C.
civilianImpact
Indicates the extent to which an action, event, or situation affects civilians, especially in terms of harm, disruption, or other consequences.
-
D.
numberOfDistrictsHeavilyDamaged
Indicates the count of districts that have sustained severe or heavy damage in a given context or event.
-
E.
areaDestroyed
Indicates that a specified portion or region has been damaged or ruined to the point of destruction.
- 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_69bd46459ff48190823377457bcf7128 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd923465c88190ad9c1b75b268f7ca |
completed | March 20, 2026, 6:30 p.m. |
| PD | Predicate disambiguation | batch_69bd91a58c448190904964a439045e05 |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:09 p.m.