Triple
T833076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bloody Friday |
E18009
|
entity |
| Predicate | typeOfViolence |
P14172
|
FINISHED |
| Object | indiscriminate bombing |
—
|
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: indiscriminate bombing | Statement: [Bloody Friday, typeOfViolence, indiscriminate bombing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfViolence Context triple: [Bloody Friday, typeOfViolence, indiscriminate bombing]
-
A.
hasTypeOfViolence
chosen
Indicates that an entity involves, exhibits, or is characterized by a specific kind or category of violent behavior or action.
-
B.
hasForcesInvolved
Indicates that specific forces, such as military or organizational units, participate in or are engaged in a particular event or operation.
-
C.
assaultedBy
Indicates that an entity has been attacked or subjected to violent physical aggression by another entity.
-
D.
portraysAsVictim
Indicates that one entity represents or depicts another entity as a victim in a given context or narrative.
-
E.
involvesWeaponType
Indicates that the relationship or action includes the use, presence, or association of a specific type or category of weapon.
- 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_69a49389f44881909a608fb27d89f247 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abb647988190950e1790bcfa60a5 |
completed | March 1, 2026, 9:12 p.m. |
| PD | Predicate disambiguation | batch_69a4aa7b3d2481909199f7c9f305bdfe |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:38 p.m.