Triple
T25600289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terminal 1 (Sofia Airport) |
E641766
|
entity |
| Predicate | otherTerminalAtSameAirport |
P54408
|
FINISHED |
| Object | Terminal 2 (Sofia 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: Terminal 2 (Sofia Airport) | Statement: [Terminal 1 (Sofia Airport), otherTerminalAtSameAirport, Terminal 2 (Sofia Airport)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: otherTerminalAtSameAirport Context triple: [Terminal 1 (Sofia Airport), otherTerminalAtSameAirport, Terminal 2 (Sofia Airport)]
-
A.
associatedAirport
Indicates a relationship where an entity is linked or connected to a specific airport, typically as its relevant or corresponding airport.
-
B.
servedByAirportInOriginCity
Indicates that the origin city of a trip or route is served by a particular airport.
-
C.
connectsWithAirport
Indicates that there is a direct transportation or operational link established between an entity and an airport.
-
D.
associatedAirportReplaced
Indicates that one airport in an association has been superseded or replaced by another airport.
-
E.
sharesAirportWith
chosen
Indicates that two entities use or are associated with the same airport.
- 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_69e75dc60d108190b7e2419e36b0134b |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f9a648308190801ef2cbab02df79 |
completed | May 2, 2026, 1:18 p.m. |
| PD | Predicate disambiguation | batch_69f5afec3e94819080d9ba86cf8c866e |
completed | May 2, 2026, 8:03 a.m. |
Created at: April 21, 2026, 4:30 p.m.