Triple
T18290542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beslan |
E438099
|
entity |
| Predicate | schoolSiegeInternationalImpact |
P131214
|
FINISHED |
| Object | widespread global condemnation |
—
|
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: widespread global condemnation | Statement: [Beslan, schoolSiegeInternationalImpact, widespread global condemnation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: schoolSiegeInternationalImpact Context triple: [Beslan, schoolSiegeInternationalImpact, widespread global condemnation]
-
A.
civilianImpact
Indicates the extent to which an action, event, or situation affects civilians, especially in terms of harm, disruption, or other consequences.
-
B.
nationalImpact
Indicates that something has a significant effect or influence at the level of an entire nation.
-
C.
schoolSpreadTo
Indicates that a school or educational institution expanded its presence or influence from one location to another.
-
D.
educationalImpact
Indicates the effect or influence that one entity has on the learning, knowledge, or educational outcomes of another.
-
E.
school
Indicates that an entity attends, is enrolled in, or is institutionally associated as a student with a particular school.
- F. None of above. chosen
Provenance (4 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_69d8b914530c8190b4474d862a2b2a1b |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e500fee5248190928e68ddaa4d90d7 |
completed | April 19, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69e44fdf43d08190bbcfb6b1fe3cc0ee |
completed | April 19, 2026, 3:45 a.m. |
| PDg | Predicate description generation | batch_69e451a0ba208190a5fe92832a8f7a49 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 10:35 a.m.