Triple
T32194160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Global Airlines |
E822353
|
entity |
| Predicate | hasFictionalRoute |
P125068
|
FINISHED |
| Object | New York to London |
—
|
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: New York to London | Statement: [Global Airlines, hasFictionalRoute, New York to London]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalRoute Context triple: [Global Airlines, hasFictionalRoute, New York to London]
-
A.
hasFictionalTransportLink
chosen
Indicates that there is a transportation connection between entities that exists only in a fictional or imagined context.
-
B.
locatedOnFictionalRoute
Indicates that something is situated along or associated with a route that exists only within a fictional or imaginary setting.
-
C.
hasFictionalDomain
Indicates that an entity is associated with or set within a fictional world, realm, or universe.
-
D.
hasFictionalFrame
Indicates that one entity is presented or interpreted within the context of a fictional narrative, scenario, or imaginative framework provided by another entity.
-
E.
hasFictionalLandmark
Indicates that one entity includes, features, or is associated with a landmark that is fictional rather than real.
- 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_69fec00f27988190955de6b6348a4d97 |
completed | May 9, 2026, 5:03 a.m. |
| PD | Predicate disambiguation | batch_69febd52037c8190b475dbd50fdbc13e |
completed | May 9, 2026, 4:51 a.m. |
Created at: May 1, 2026, 12:35 a.m.