Triple
T5099609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RE |
E114950
|
entity |
| Predicate | typicalStops |
P43131
|
FINISHED |
| Object | major regional stations |
—
|
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: major regional stations | Statement: [RE, typicalStops, major regional stations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalStops Context triple: [RE, typicalStops, major regional stations]
-
A.
typicalStopoverCity
Indicates that a city commonly serves as an intermediate stop or layover point in a journey between other locations.
-
B.
typicalStopoverRegion
chosen
Indicates the geographic region where an entity (such as a migrating animal or traveler) most commonly makes an intermediate stop during its journey.
-
C.
tourStopOf
Indicates that one entity is a stop or scheduled visit location within the itinerary or route of another entity’s tour.
-
D.
typicalTimes
Indicates the usual or characteristic times at which an event, activity, or condition typically occurs.
-
E.
typicalDestinationAirportIATA
Indicates the IATA airport code that is typically the destination in this kind of trip or route.
- 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_69bd443fc49c819089629c00e311310c |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7568e9c881909f114973faef6832 |
completed | March 20, 2026, 4:27 p.m. |
| PD | Predicate disambiguation | batch_69bd715e06808190931934dc9930f997 |
completed | March 20, 2026, 4:10 p.m. |
Created at: March 20, 2026, 1:40 p.m.