Triple
T11749921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Chinese Famine |
E279377
|
entity |
| Predicate | deathTollRanking |
P101140
|
FINISHED |
| Object | one of the deadliest famines in human history |
—
|
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: one of the deadliest famines in human history | Statement: [Great Chinese Famine, deathTollRanking, one of the deadliest famines in human history]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: deathTollRanking Context triple: [Great Chinese Famine, deathTollRanking, one of the deadliest famines in human history]
-
A.
deathTollEstimate
Indicates an estimated number of deaths attributed to a particular event, cause, or period.
-
B.
deathToll
Indicates the number of deaths resulting from a particular event, situation, or cause.
-
C.
countryMostVictimsFrom
Indicates the country from which the largest number of victims in a given event, situation, or context originate.
-
D.
causeOfDeath
Indicates the specific factor, event, or condition that directly resulted in an entity’s death.
-
E.
fatalitiesCategory
Indicates the classification of deaths associated with an event, incident, or condition into a specific category or severity level.
- 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_69d6ab01038c819080714901502c84fc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a508b0c4819082fbcc27d559ea2f |
completed | April 10, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69d88a813cc48190a3dfdc60e8af80ae |
completed | April 10, 2026, 5:28 a.m. |
| PDg | Predicate description generation | batch_69d890458d948190b15054c9ba0fd923 |
completed | April 10, 2026, 5:53 a.m. |
Created at: April 8, 2026, 9:41 p.m.