Triple
T9849439
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agadir Oufella (Kasbah) |
E239426
|
entity |
| Predicate | damageInEvent |
P81550
|
FINISHED |
| Object | largely destroyed in the 1960 Agadir earthquake |
—
|
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: largely destroyed in the 1960 Agadir earthquake | Statement: [Agadir Oufella (Kasbah), damageInEvent, largely destroyed in the 1960 Agadir earthquake]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: damageInEvent Context triple: [Agadir Oufella (Kasbah), damageInEvent, largely destroyed in the 1960 Agadir earthquake]
-
A.
damageLeadsTo
Indicates that one instance of damage causally results in or contributes to another specified outcome or condition.
-
B.
damageTo
Indicates a relationship where one entity causes harm, loss, or deterioration to another entity.
-
C.
damageAssociatedWith
Indicates a relationship where one entity is linked to causing, contributing to, or being responsible for damage affecting another entity.
-
D.
damageDescription
chosen
Indicates a textual description of the nature, extent, or characteristics of damage associated with an entity or event.
-
E.
damageBasis
Indicates the underlying reason, cause, or basis on which damage is determined or assessed in a given context.
- 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_69ca84e4fdc08190a624425bcef98665 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb371894c8190971ba497a2801521 |
completed | April 2, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69cd03e57cac8190914bb5ae608a6e0e |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:34 p.m.