Triple
T4444197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frankfurt Egelsbach Airport |
E96239
|
entity |
| Predicate | hasFlightSchools |
P34146
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Frankfurt Egelsbach Airport, hasFlightSchools, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFlightSchools Context triple: [Frankfurt Egelsbach Airport, hasFlightSchools, true]
-
A.
hasGeneralAviationActivity
Indicates that an entity is involved in or supports non-commercial, private, or recreational aviation operations.
-
B.
hasFixedWingTrainingRole
chosen
Indicates that an entity serves in a training capacity specifically related to the operation or use of fixed-wing aircraft.
-
C.
hasGeneralAviationFacilities
Indicates that a location or airport provides facilities and services specifically for general aviation operations.
-
D.
hasAirlines
Indicates that one entity (such as an airport, city, or country) is served by or associated with one or more airline operators.
-
E.
hasSnowSportsSchool
Indicates that a place or facility offers an organized school or program for learning and practicing snow sports.
- 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_69b345415ba481908df738e7174448ba |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b355b052688190a0d8e5912f82151c |
completed | March 13, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69b34f62c180819097ced38da2052207 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:32 p.m.