Triple
T36273660
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2023 Marrakesh–Safi earthquake |
E892742
|
entity |
| Predicate | worstAffectedArea |
P1586
|
FINISHED |
| Object | Al Haouz Province |
—
|
NE NERFINISHED |
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: Al Haouz Province | Statement: [2023 Marrakesh–Safi earthquake, worstAffectedArea, Al Haouz Province]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: worstAffectedArea Context triple: [2023 Marrakesh–Safi earthquake, worstAffectedArea, Al Haouz Province]
-
A.
affectedArea
chosen
Indicates the specific region or extent over which an event, condition, or influence has an impact.
-
B.
sectorMostAffected
Indicates that a particular sector is the one experiencing the greatest impact or disruption relative to others in a given context.
-
C.
affectsAnatomicalLocation
Indicates that one entity produces an effect on, or has an impact at, a specific anatomical location.
-
D.
worstAffectedVillage
Indicates that the village is the one most severely impacted or damaged in a given event or situation.
-
E.
involvedLocation
Indicates that an event, action, or relationship takes place in, or is significantly associated with, a particular location.
- 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_69f76e488f34819083e254dbe288c27a |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7ba6d06f48190a71b5a2f19e2232f |
completed | May 3, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69f7b9a4aad48190a62e41c5e39339d9 |
completed | May 3, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:09 p.m.