Triple
T316313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Domodedovo International Airport |
E7713
|
entity |
| Predicate | belongsToAirportSystem |
P11953
|
FINISHED |
| Object | Moscow airport system |
—
|
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: Moscow airport system | Statement: [Domodedovo International Airport, belongsToAirportSystem, Moscow airport system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: belongsToAirportSystem Context triple: [Domodedovo International Airport, belongsToAirportSystem, Moscow airport system]
-
A.
airportServed
Indicates that a particular airport provides service to, or is used for air travel to and from, a given location or area.
-
B.
airlineHub
Indicates that a particular location (typically an airport or city) serves as a central hub or primary operational base for an airline.
-
C.
servesAirport
Indicates that a transportation service or route provides access to and operates for a particular airport.
-
D.
airportRole
Indicates that an entity serves a specific functional role or capacity within the context of an airport.
-
E.
appliesToAirspaceOf
Indicates that a rule, restriction, or condition is specifically relevant to, or in effect within, a particular airspace.
- F. None of above. chosen
Provenance (4 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea6462148190825acc57f6d2adaf |
completed | Feb. 28, 2026, 1:15 p.m. |
| PD | Predicate disambiguation | batch_69a2e943f12c8190883854aeed974260 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea08878c8190a5e8a90f620a3888 |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.