Triple
T32194082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Global Airlines Flight 33 |
E822351
|
entity |
| Predicate | passengersIncluded |
P159089
|
FINISHED |
| Object | fictional passengers |
—
|
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: fictional passengers | Statement: [Global Airlines Flight 33, passengersIncluded, fictional passengers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengersIncluded Context triple: [Global Airlines Flight 33, passengersIncluded, fictional passengers]
-
A.
passengers
Indicates that one entity is traveling in or being transported by another entity, typically as a non-operating occupant.
-
B.
passengersType
chosen
Indicates the type or category of passengers associated with or involved in a given entity or context.
-
C.
isPassengerWith
Indicates that one entity is traveling together with another entity as a passenger in the same vehicle or conveyance.
-
D.
passengerCount
Indicates the number of passengers associated with a given entity, such as a vehicle or trip.
-
E.
hasThroughPassengersWith
Indicates that two transportation segments, services, or locations are connected by passengers who travel through them without starting or ending their journey there.
- 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_69f3490819cc81909bae1f8ce99423c5 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f72921cf2c8190909bb53f78bcc890 |
completed | May 3, 2026, 10:53 a.m. |
| PD | Predicate disambiguation | batch_69f7283d8cec8190b524c144948bc4ec |
completed | May 3, 2026, 10:49 a.m. |
Created at: May 1, 2026, 12:35 a.m.