Triple
T35790553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lights over Phoenix |
E1034680
|
entity |
| Predicate | airspace |
P22328
|
FINISHED |
| Object | Arizona |
—
|
NE NERFINISHED |
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: Arizona | Statement: [Lights over Phoenix, airspace, Arizona]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airspace Context triple: [Lights over Phoenix, airspace, Arizona]
-
A.
airspaceType
Indicates the classification or category of airspace that applies to a given location or flight segment.
-
B.
hasAirspace
chosen
Indicates that one entity possesses, controls, or is associated with a defined region of airspace relative to another entity or area.
-
C.
separatesAirspaceOf
Indicates a relationship where one entity divides or delineates the airspace of two or more other entities, creating distinct separated airspace regions between them.
-
D.
airspaceRole
Indicates the specific function or responsibility an entity has within a defined airspace.
-
E.
appliesToAirspaceOf
Indicates that a rule, restriction, or condition is specifically relevant to, or in effect within, a particular airspace.
- 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_69f76e1575908190aaa306d843b41c14 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a22e0ff88190989a5df84fca968b |
completed | May 3, 2026, 7:29 p.m. |
| PD | Predicate disambiguation | batch_69f7a070e23881909a233370acb57384 |
completed | May 3, 2026, 7:22 p.m. |
Created at: May 3, 2026, 4:06 p.m.