Triple
T4884518
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Government of Lebanon |
E109406
|
entity |
| Predicate | crisisContext |
P59615
|
FINISHED |
| Object | has faced recurrent political deadlocks |
—
|
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: has faced recurrent political deadlocks | Statement: [Government of Lebanon, crisisContext, has faced recurrent political deadlocks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: crisisContext Context triple: [Government of Lebanon, crisisContext, has faced recurrent political deadlocks]
-
A.
crisisRelatedTo
Indicates a relationship where one situation, event, or condition is connected to, associated with, or relevant to a crisis.
-
B.
majorCrisis
Indicates a severe, high-impact crisis or emergency situation affecting an entity or system.
-
C.
context
Indicates that one entity provides the surrounding circumstances, setting, or background within which another entity, event, or statement occurs or is interpreted.
-
D.
conflictContext
Indicates the situational background or circumstances within which a conflict between entities occurs or is interpreted.
-
E.
crisisPeakPeriod
Indicates the time period during which a crisis reaches its highest intensity or most critical phase.
- 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_69bd440f71348190b99938e59fb7f9a1 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6de253ac8190b1112da6953fa4f2 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c2be5e881909f6ec9c3bcde49f3 |
completed | March 20, 2026, 3:47 p.m. |
| PDg | Predicate description generation | batch_69bd6d5976a081909090c0c263f6e9b7 |
completed | March 20, 2026, 3:52 p.m. |
Created at: March 20, 2026, 1:27 p.m.