Triple
T3870218
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SCSE |
E91965
|
entity |
| Predicate | associatedAirportType |
P16113
|
FINISHED |
| Object | public airport |
—
|
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: public airport | Statement: [SCSE, associatedAirportType, public airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedAirportType Context triple: [SCSE, associatedAirportType, public airport]
-
A.
associatedWithAirportType
chosen
Indicates that an entity has a connection or linkage to a specific category or type of airport.
-
B.
associatedWithAirportCode
Indicates that one entity has a relationship or connection to an airport identified by a specific airport code.
-
C.
belongsToAirport
Indicates that one entity is part of, associated with, or under the jurisdiction of a specific airport.
-
D.
hasAirportCodeType
Indicates that an airport code is associated with a specific classification or type (e.g., IATA, ICAO, FAA).
-
E.
associatedWithAirportName
Indicates a relationship where an entity is linked or connected to a specific airport by its name.
- 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_69aed9645f348190a9868e7cef56ab7e |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec533828819080f52dae15fdbecd |
completed | March 9, 2026, 3:50 p.m. |
| PD | Predicate disambiguation | batch_69aee754dddc8190936e1f9c40a770db |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:20 p.m.