Triple
T3393817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Albany International Airport |
E71479
|
entity |
| Predicate | hasAirportHotelNearby |
P49385
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Albany International Airport, hasAirportHotelNearby, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAirportHotelNearby Context triple: [Albany International Airport, hasAirportHotelNearby, yes]
-
A.
airportLocatedNear
Indicates that an airport is situated close to a specified place or geographic feature.
-
B.
nearestAirport
Indicates that one airport is the closest in distance to a given location or entity compared to all other airports.
-
C.
hasAttractionNearby
Indicates that one entity is located close to another entity that serves as an attraction or point of interest.
-
D.
airportLocatedWithin
Indicates that an airport is geographically situated inside the boundaries of a specified area or region.
-
E.
hasEndpointAirport
Indicates that something, such as a route or flight, has a specific airport as one of its terminal endpoints.
- F. None of above. chosen
Provenance (4 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_69ad85a9c4a88190a854019341cb3b60 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb853746c8190bfa1447e6ebbefb3 |
completed | March 8, 2026, 5:56 p.m. |
| PD | Predicate disambiguation | batch_69adadf705608190975423779430cc58 |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adb2e426b88190b82d9830149b142e |
completed | March 8, 2026, 5:33 p.m. |
Created at: March 8, 2026, 3:14 p.m.