Triple
T10517384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Class B airspace |
E248068
|
entity |
| Predicate | hasTypicalExamples |
P12230
|
FINISHED |
| Object | airspace around Los Angeles International 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: airspace around Los Angeles International Airport | Statement: [Class B airspace, hasTypicalExamples, airspace around Los Angeles International Airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalExamples Context triple: [Class B airspace, hasTypicalExamples, airspace around Los Angeles International Airport]
-
A.
hasExample
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
B.
hasTypicalSubject
Indicates that something is commonly or characteristically used as the subject (agent or topic) of a given relation or action.
-
C.
typicalIn
chosen
Indicates that something commonly occurs, appears, or is found within a given context, category, or environment.
-
D.
hasNonExample
Indicates that something is associated with an instance that explicitly does not satisfy or illustrate a given concept, rule, or category.
-
E.
usedAsExampleIn
Indicates that one entity is cited or presented as an illustrative example within another entity, such as a text, discussion, or explanation.
- 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_69d381c4aa948190942e1d803143fb0e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509cd0fb8819087de2f9a93bad6e6 |
completed | April 7, 2026, 1:42 p.m. |
| PD | Predicate disambiguation | batch_69d4fb94fa10819091f585bab4379c6f |
completed | April 7, 2026, 12:41 p.m. |
Created at: April 6, 2026, 12:28 p.m.