Triple
T21820364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Machhu Dam |
E538708
|
entity |
| Predicate | casualtiesInNotableEvent |
P10775
|
FINISHED |
| Object | thousands of deaths in 1979 flood |
—
|
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 in 1979 flood | Statement: [Machhu Dam, casualtiesInNotableEvent, thousands of deaths in 1979 flood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesInNotableEvent Context triple: [Machhu Dam, casualtiesInNotableEvent, thousands of deaths in 1979 flood]
-
A.
casualtiesAssociatedWithEvent
Indicates that certain casualties (deaths or injuries) are linked to, or resulted from, a specific event.
-
B.
notableDeathTollEvent
Indicates that an event is characterized by causing an unusually large or historically significant number of deaths.
-
C.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
D.
casualtiesDescription
chosen
Indicates a textual description of the human losses (such as deaths, injuries, or missing persons) resulting from an event or incident.
-
E.
nativeCasualties
Indicates that native or indigenous people suffered deaths or injuries as a result of a particular event, action, or conflict.
- 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_69e0c475038c8190abb9b1a20eb8ff50 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f0912d414c81909c109c3e45b6e7d2 |
completed | April 28, 2026, 10:51 a.m. |
| PD | Predicate disambiguation | batch_69e6be815a108190be81d7c987d0c0d6 |
completed | April 21, 2026, 12:02 a.m. |
Created at: April 16, 2026, 6:54 p.m.