Triple
T4972495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1948 Donora smog |
E111686
|
entity |
| Predicate | numberOfPeopleAffected |
P54084
|
FINISHED |
| Object | thousands |
—
|
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 | Statement: [1948 Donora smog, numberOfPeopleAffected, thousands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPeopleAffected Context triple: [1948 Donora smog, numberOfPeopleAffected, thousands]
-
A.
estimatedAffectedPeople
chosen
Indicates the estimated number of people expected to be impacted by a particular event, condition, or action.
-
B.
affectedPeople
Indicates the people who are impacted or influenced by a particular event, action, or condition.
-
C.
estimatedVictimsUnderAuthority
Indicates that a specified authority is estimated to have a certain number of victims under its control, influence, or jurisdiction.
-
D.
estimatedNumberOfPeopleSaved
Indicates the approximate count of individuals whose lives were preserved or harm was averted as a result of a particular action, intervention, or entity.
-
E.
affectedPerson
Indicates that a particular person is impacted or influenced by an 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_69bd441a0eb481908050fa4273b19eae |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd730a7590819088ab8d49c5c88c2f |
completed | March 20, 2026, 4:17 p.m. |
| PD | Predicate disambiguation | batch_69bd7146e6e881908a55ab2756b631f6 |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:33 p.m.