Triple
T12893507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lorrie Sullenberger – Laura Linney |
E308428
|
entity |
| Predicate | linkedToRealEvent |
P59649
|
FINISHED |
| Object | US Airways Flight 1549 water landing |
—
|
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: US Airways Flight 1549 water landing | Statement: [Lorrie Sullenberger – Laura Linney, linkedToRealEvent, US Airways Flight 1549 water landing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: linkedToRealEvent Context triple: [Lorrie Sullenberger – Laura Linney, linkedToRealEvent, US Airways Flight 1549 water landing]
-
A.
isLinkedToEvent
chosen
Indicates that an entity has an association or connection with a specific event.
-
B.
laterRelatedEvent
Indicates that one event is temporally related to another by occurring at a later time.
-
C.
recordsEventsVia
Indicates that one entity captures or logs events through the mechanism, system, or medium provided by another entity.
-
D.
closelyAssociatedEvent
Indicates that one event is strongly related to, connected with, or occurs in close conjunction with another event.
-
E.
connectedToEvent
Indicates that an entity has a direct association or linkage with a specific event.
- 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971484aa08190a8adfafabe600903 |
completed | April 10, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69d96fa776648190b9b5c30722ea50b6 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:40 p.m.