Triple
T19951751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Mon restaurant bombing |
E479571
|
entity |
| Predicate | impactOnIRA |
P103390
|
FINISHED |
| Object | increased criticism of Provisional IRA tactics |
—
|
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: increased criticism of Provisional IRA tactics | Statement: [La Mon restaurant bombing, impactOnIRA, increased criticism of Provisional IRA tactics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactOnIRA Context triple: [La Mon restaurant bombing, impactOnIRA, increased criticism of Provisional IRA tactics]
-
A.
impactDescription
chosen
Indicates a description of the effect, consequence, or influence that one entity, action, or event has on another.
-
B.
impactStatus
Indicates the current state or condition of how something has affected or influenced a target.
-
C.
impactCategory
Indicates the type or domain of effect that one entity or action has on another, classifying the nature of its impact.
-
D.
impactOnIran
Indicates the effect or consequences that an action, event, or policy has on Iran.
-
E.
impactIfCompleted
Indicates the effect or consequence that will occur if the referenced task or action is fully completed.
- 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_69d8e522a17c819095165d4d24939fd8 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65a6c87388190a1bada3117acaf7b |
completed | April 20, 2026, 4:55 p.m. |
| PD | Predicate disambiguation | batch_69e537f47c508190853c4e009c6b5566 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:54 p.m.