Triple
T4848631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tbilisi International Airport |
E108354
|
entity |
| Predicate | languageUsedAtAirport |
P18209
|
FINISHED |
| Object | Georgian |
—
|
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: Georgian | Statement: [Tbilisi International Airport, languageUsedAtAirport, Georgian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageUsedAtAirport Context triple: [Tbilisi International Airport, languageUsedAtAirport, Georgian]
-
A.
languageUsedInTourism
Indicates that a particular language is used for communication and services within tourism activities or contexts.
-
B.
languagesSpoken
Indicates that an entity is able to communicate using one or more specified languages.
-
C.
languageUse
chosen
Indicates the language or languages an entity uses for communication, expression, or interaction.
-
D.
usesAirportCode
Indicates that one entity employs or identifies an airport by a specific standardized airport code.
-
E.
isWidelySpokenIn
Indicates that a language is spoken by a large portion of the population across many regions or communities within a specified area.
- 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_69bd4409b264819085ab855f3eb5381a |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ddd17d881909f7731ff2b460e83 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c2557388190a2d15571bacd24f3 |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:25 p.m.