Triple
T27769732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SouthJet Airlines |
E701708
|
entity |
| Predicate | hasFictionalAccident |
P188772
|
FINISHED |
| Object | SouthJet Flight 227 |
—
|
NE NERFINISHED |
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: SouthJet Flight 227 | Statement: [SouthJet Airlines, hasFictionalAccident, SouthJet Flight 227]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalAccident Context triple: [SouthJet Airlines, hasFictionalAccident, SouthJet Flight 227]
-
A.
hasAccidentAt
Indicates that an accident involving a subject occurs at a specific location or time.
-
B.
fatalAccident
Indicates that an accident resulted in at least one death.
-
C.
involvedInAccident
Indicates that an entity participated in, was affected by, or was otherwise a party to a specific accident or collision event.
-
D.
hasInjuredPerson
Indicates that an entity has a person who has been harmed or injured associated with it.
-
E.
causedAccident
Indicates that one entity is responsible for bringing about or initiating an accident involving another entity or situation.
- 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_69ef6a52fa708190934a32308d2c92dc |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69fbad1e94988190b86d447a68e65067 |
completed | May 6, 2026, 9:05 p.m. |
| PD | Predicate disambiguation | batch_69fba881b8e0819094790935152b99a1 |
completed | May 6, 2026, 8:45 p.m. |
| PDg | Predicate description generation | batch_69fbad1b3ba08190ad69e21461333f2e |
completed | May 6, 2026, 9:05 p.m. |
Created at: April 27, 2026, 4:34 p.m.