Triple
T35568877
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RJCM |
E1027854
|
entity |
| Predicate | mapsToAirportRunwayDirectionSystem |
P149490
|
FINISHED |
| Object | Memanbetsu Airport |
—
|
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: Memanbetsu Airport | Statement: [RJCM, mapsToAirportRunwayDirectionSystem, Memanbetsu Airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mapsToAirportRunwayDirectionSystem Context triple: [RJCM, mapsToAirportRunwayDirectionSystem, Memanbetsu Airport]
-
A.
runwayInformationAvailableIn
Indicates that information about a runway is available within or through a specified medium, source, or context.
-
B.
associatedWithRunways
Indicates a relationship where something (such as an object, facility, or feature) is linked or connected to one or more runways.
-
C.
runwayPlan
Indicates a planned or designated use of a runway for aircraft operations (such as takeoff, landing, or sequencing) within an airfield’s operational schedule.
-
D.
mapsToAirport
chosen
Indicates a relationship where one entity is associated with, linked to, or directed toward a specific airport.
-
E.
numberOfRunways
Indicates the quantity of runways associated with a given entity, such as an airport or airfield.
- 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_69f76e020fd8819081cb080e7e203083 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79ec355048190af30123ceb6efa2b |
completed | May 3, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69f79e4bdbcc8190be7a0d2cf8a77b64 |
completed | May 3, 2026, 7:13 p.m. |
Created at: May 3, 2026, 4:04 p.m.