Triple
T16268218
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orlando Executive Airport |
E394927
|
entity |
| Predicate | proximityToDowntown |
P34630
|
FINISHED |
| Object | near downtown Orlando |
—
|
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: near downtown Orlando | Statement: [Orlando Executive Airport, proximityToDowntown, near downtown Orlando]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: proximityToDowntown Context triple: [Orlando Executive Airport, proximityToDowntown, near downtown Orlando]
-
A.
distanceFromDowntown
Indicates the physical distance between a given location and the central downtown area.
-
B.
nearDowntown
chosen
Indicates that one location is situated close to or within a short distance of a city’s downtown area.
-
C.
proximityToLandmark
Indicates a spatial relationship where one entity is located near or close to a specified landmark.
-
D.
isLocatedDowntown
Indicates that an entity is situated within the downtown area of a city or town.
-
E.
hasUrbanProximity
Indicates that one entity is located near or within easy access to an urban area associated with another entity.
- 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_69d87f221d8081909b0b2063e7528ba2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e245ca5c708190a1e98ab37740c032 |
completed | April 17, 2026, 2:38 p.m. |
| PD | Predicate disambiguation | batch_69e219f259e88190bf49d8408c04178e |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:05 a.m.