Triple
T7371643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Louis Downtown Airport |
E170017
|
entity |
| Predicate | servesBusinessAviationFor |
P71775
|
FINISHED |
| Object | downtown St. Louis |
—
|
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: downtown St. Louis | Statement: [St. Louis Downtown Airport, servesBusinessAviationFor, downtown St. Louis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesBusinessAviationFor Context triple: [St. Louis Downtown Airport, servesBusinessAviationFor, downtown St. Louis]
-
A.
servesAviationType
chosen
Indicates that one entity provides services or functions specifically for a particular type or category of aviation.
-
B.
servesAirTaxi
Indicates that one entity provides air taxi transportation services to or on behalf of another entity.
-
C.
servesBusinessTravel
Indicates that an entity provides services or accommodations specifically intended for business-related travel.
-
D.
servesAirportType
Indicates that a transportation service or facility provides service to, or is designated for, a specific type or category of airport.
-
E.
servesAirport
Indicates that a transportation service or route provides access to and operates for a particular 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_69c68a5bfaac81909ce7f001dfb70c76 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f18451d88190ad4a2674279bb703 |
completed | March 27, 2026, 9:07 p.m. |
| PD | Predicate disambiguation | batch_69c6f02ee3e08190a7a00c981129b22c |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:07 p.m.