Triple
T28655104
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karachi–London |
E725308
|
entity |
| Predicate | hasStopoverCommonlyIn |
P43132
|
FINISHED |
| Object | Dubai |
—
|
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: Dubai | Statement: [Karachi–London, hasStopoverCommonlyIn, Dubai]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStopoverCommonlyIn Context triple: [Karachi–London, hasStopoverCommonlyIn, Dubai]
-
A.
hasStopoverState
Indicates that an entity’s journey or process includes an intermediate stop or temporary state before reaching its final destination or outcome.
-
B.
isStopoverPoint
Indicates that a location serves as an intermediate stopping point along a journey or route, rather than the final destination.
-
C.
typicalStopoverCity
chosen
Indicates that a city commonly serves as an intermediate stop or layover point in a journey between other locations.
-
D.
hasStops
Indicates that a route, service, or journey includes one or more intermediate stopping points at specified locations.
-
E.
hasPassengerTransfers
Indicates that passengers move or are transferred from one vehicle, route, or segment of a journey to another.
- 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_69f01d84f5f0819087ab5e6143b14ed7 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69fd5d48855c8190bd93070b6a00d8b5 |
completed | May 8, 2026, 3:49 a.m. |
| PD | Predicate disambiguation | batch_69fd5c9aabb88190912800d90184a89d |
completed | May 8, 2026, 3:46 a.m. |
Created at: April 28, 2026, 4:54 a.m.