Triple
T27769724
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SouthJet Airlines |
E701708
|
entity |
| Predicate | hasFictionalEmployeeRole |
P61558
|
FINISHED |
| Object | airline pilot |
—
|
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: airline pilot | Statement: [SouthJet Airlines, hasFictionalEmployeeRole, airline pilot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalEmployeeRole Context triple: [SouthJet Airlines, hasFictionalEmployeeRole, airline pilot]
-
A.
hasFictionalStaffMember
chosen
Indicates that an entity includes or employs a staff member who is a fictional character.
-
B.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
C.
hasHumanCharacterRole
Indicates that an entity is assigned a role or function specifically associated with a human character within a context such as a story, performance, or representation.
-
D.
hasFictionalProfessionLevel
Indicates that an entity holds a fictional or imagined profession at a specified level, rank, or degree of expertise.
-
E.
hasPlayingRole
Indicates that an entity participates in an activity, event, or performance in a specific playing role or capacity.
- 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_69ef6a52fa708190934a32308d2c92dc |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69fba78aca4c8190b8f1831e8cc04e06 |
completed | May 6, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69fba34a65a4819088bac6c17542d71c |
completed | May 6, 2026, 8:23 p.m. |
Created at: April 27, 2026, 4:34 p.m.