Triple
T34626984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MSO |
E889163
|
entity |
| Predicate | categoryFAA |
P148632
|
FINISHED |
| Object | primary commercial service airport |
—
|
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: primary commercial service airport | Statement: [MSO, categoryFAA, primary commercial service airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: categoryFAA Context triple: [MSO, categoryFAA, primary commercial service airport]
-
A.
FAAclassification
chosen
Indicates the type or category assigned to something under the Federal Aviation Administration’s (FAA) classification system.
-
B.
FAAcode
Indicates that an entity (such as an airport or facility) is associated with a specific identifying code assigned by the Federal Aviation Administration (FAA).
-
C.
airworthinessCategory
Indicates the regulatory airworthiness classification assigned to an aircraft or component, defining the standards and conditions under which it is approved to operate.
-
D.
targetAircraftCategory
Indicates the category or type of aircraft that is the intended target of an action or operation.
-
E.
typeOfAviation
Indicates the specific category or kind of aviation to which an entity belongs (e.g., commercial, military, private).
- 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_69f349d64a388190a013cfa9bd33fad7 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f727bde8f88190ad746ca515134ca1 |
completed | May 3, 2026, 10:47 a.m. |
| PD | Predicate disambiguation | batch_69f72739c30c81908642eef3feb3afcf |
completed | May 3, 2026, 10:45 a.m. |
Created at: May 1, 2026, 2:04 a.m.