Triple
T25600288
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terminal 1 (Sofia Airport) |
E641766
|
entity |
| Predicate | numberOfPassengerTerminalsAtAirport |
P98033
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Terminal 1 (Sofia Airport), numberOfPassengerTerminalsAtAirport, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPassengerTerminalsAtAirport Context triple: [Terminal 1 (Sofia Airport), numberOfPassengerTerminalsAtAirport, 2]
-
A.
hasNumberOfPassengerTerminalsAtAirport
chosen
Indicates the relationship that specifies how many passenger terminals are present at a given airport.
-
B.
airportHasTerminals
Indicates that a particular airport includes or is composed of one or more terminal facilities.
-
C.
isLargestPassengerTerminalOf
Indicates that one entity is the largest passenger terminal within or associated with another entity (such as a transport hub or system).
-
D.
hasPassengerTerminalFacilities
Indicates that an entity provides facilities or infrastructure specifically intended for handling and serving passengers.
-
E.
hasPassengerBoardingGates
Indicates that an entity is associated with or contains one or more passenger boarding gates used for embarking or disembarking passengers.
- 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_69f7b5ccbda481908fe1945c35e36ce8 |
completed | May 3, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69f7b4c06f5881908f0b98cad6796478 |
completed | May 3, 2026, 8:49 p.m. |
Created at: April 21, 2026, 4:30 p.m.