Triple
T19768124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meron, Israel |
E474810
|
entity |
| Predicate | hasEventRisk |
P136373
|
FINISHED |
| Object | crowd safety concerns during mass gatherings |
—
|
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: crowd safety concerns during mass gatherings | Statement: [Meron, Israel, hasEventRisk, crowd safety concerns during mass gatherings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEventRisk Context triple: [Meron, Israel, hasEventRisk, crowd safety concerns during mass gatherings]
-
A.
hasRiskFrom
chosen
Indicates that one entity is exposed to or may suffer potential harm, loss, or adverse effects as a result of another entity.
-
B.
hasPregnancyRisk
Indicates that one entity poses or is associated with a potential risk of causing pregnancy for another entity.
-
C.
hasEnvironmentalRisk
Indicates that an entity poses, contributes to, or is associated with potential harm or adverse impact on the environment.
-
D.
hazardEvent
Indicates an occurrence of a dangerous or harmful event that poses a risk or threat within a given context.
-
E.
hadEvent
Indicates that an entity experienced, hosted, or was associated with a specific event at some point in time.
- 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_69d8e51a43a08190956bc6df13c91a77 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65358fc3c8190867fea2a2c4e7594 |
completed | April 20, 2026, 4:24 p.m. |
| PD | Predicate disambiguation | batch_69e5305016e08190b9561a96baecb0b8 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:48 p.m.