Triple
T32402320
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Armero tragedy |
E827984
|
entity |
| Predicate | typeOfLahar |
P95449
|
FINISHED |
| Object | cold lahar (meltwater and debris) |
—
|
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: cold lahar (meltwater and debris) | Statement: [Armero tragedy, typeOfLahar, cold lahar (meltwater and debris)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfLahar Context triple: [Armero tragedy, typeOfLahar, cold lahar (meltwater and debris)]
-
A.
hasLahars
chosen
Indicates that an entity experiences, produces, or is associated with volcanic mudflows (lahars).
-
B.
hasPotentialForLahars
Indicates that one entity (typically a geographic or volcanic feature) is capable of producing or being affected by lahars under certain conditions.
-
C.
typicalLavaType
Indicates the usual or characteristic type of lava associated with a given volcanic feature, eruption, or context.
-
D.
oneOfDeadliestLaharDisasters
Indicates that the event is among the most deadly lahar (volcanic mudflow) disasters in terms of human casualties or impact.
-
E.
typeOfVolcanicFeature
Indicates that one entity is classified as a specific kind or category of volcanic feature represented by the other entity.
- 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_69f34919342c8190a4c3bf35a90d4e58 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a0058c341ac8190825067dc25158839 |
completed | May 10, 2026, 10:06 a.m. |
| PD | Predicate disambiguation | batch_6a005857249c81908b27587b84d84dbb |
completed | May 10, 2026, 10:05 a.m. |
Created at: May 1, 2026, 12:52 a.m.