Triple
T2442723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SMF |
E53313
|
entity |
| Predicate | airportUse |
P38514
|
FINISHED |
| Object | commercial |
—
|
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: commercial | Statement: [SMF, airportUse, commercial]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airportUse Context triple: [SMF, airportUse, commercial]
-
A.
airportServesAs
Indicates that an airport functions in a particular role or capacity (such as primary, secondary, or hub) for a specified area, organization, or service.
-
B.
airportStation
Indicates a location functions as an airport facility where air transport operations occur.
-
C.
airportIATAAssociated
Indicates that an airport is associated with a specific IATA (International Air Transport Association) code.
-
D.
airportServed
Indicates that a particular airport provides service to, or is used for air travel to and from, a given location or area.
-
E.
airportLocatedNear
Indicates that an airport is situated close to a specified place or geographic feature.
- 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_69ab495b6dac8190ac82661aa1452222 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abcabf477081908512df63ac1a6414 |
completed | March 7, 2026, 6:50 a.m. |
| PD | Predicate disambiguation | batch_69abc5ad8e588190b97c4cd7cf575043 |
completed | March 7, 2026, 6:29 a.m. |
| PDg | Predicate description generation | batch_69abcabe646c8190855d13b5dff07b97 |
completed | March 7, 2026, 6:50 a.m. |
Created at: March 6, 2026, 9:43 p.m.