Triple
T11901940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moro Gulf |
E283172
|
entity |
| Predicate | 1976MoroGulfEarthquakeCasualties |
P102148
|
FINISHED |
| Object | thousands of deaths |
—
|
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: thousands of deaths | Statement: [Moro Gulf, 1976MoroGulfEarthquakeCasualties, thousands of deaths]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 1976MoroGulfEarthquakeCasualties Context triple: [Moro Gulf, 1976MoroGulfEarthquakeCasualties, thousands of deaths]
-
A.
notableEarthquake
Indicates that an earthquake event is significant or noteworthy due to its magnitude, impact, or historical importance.
-
B.
facedMajorEarthquake
Indicates that an entity has experienced or been subjected to a significant or severe earthquake event.
-
C.
populationImpact2010Earthquake
Indicates the effect or consequences that the 2010 earthquake had on a population, such as changes in size, distribution, or demographic characteristics.
-
D.
experiencedMajorTsunami
Indicates that the subject has undergone or been affected by a large-scale, significant tsunami event.
-
E.
producedTsunami
Indicates that an event or phenomenon caused or generated a tsunami as a consequence.
- 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_69d6ab2c07e88190ba13b0d21fd6cf33 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8dd1792648190853f15fbf217eebd |
completed | April 10, 2026, 11:20 a.m. |
| PD | Predicate disambiguation | batch_69d8bb2fca4481909893f3428b0871ac |
completed | April 10, 2026, 8:56 a.m. |
| PDg | Predicate description generation | batch_69d8d399d58c81908dab572aa82426d7 |
completed | April 10, 2026, 10:40 a.m. |
Created at: April 8, 2026, 9:44 p.m.