Triple
T32140262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Managua earthquake of 1972 |
E820881
|
entity |
| Predicate | areaDevastated |
P18719
|
FINISHED |
| Object | downtown Managua |
—
|
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: downtown Managua | Statement: [Managua earthquake of 1972, areaDevastated, downtown Managua]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaDevastated Context triple: [Managua earthquake of 1972, areaDevastated, downtown Managua]
-
A.
areaDestroyed
chosen
Indicates that a specified portion or region has been damaged or ruined to the point of destruction.
-
B.
destroyedCity
Indicates that an entity has caused the complete or near-complete destruction of a city.
-
C.
numberOfDistrictsHeavilyDamaged
Indicates the count of districts that have sustained severe or heavy damage in a given context or event.
-
D.
numberOfDistrictsDestroyed
Indicates the quantity of districts that have been destroyed in a given context or event.
-
E.
affectedLand
Indicates that a piece of land is impacted or influenced by a particular event, action, or condition.
- 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_69f349039e0c819091c7a7d322e3f46d |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b9ae194c8190938f830fa30e623a |
completed | May 3, 2026, 2:57 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a970b0819090c6473844ffa8e3 |
completed | May 3, 2026, 2:32 a.m. |
Created at: May 1, 2026, 12:30 a.m.