Triple
T2088458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SAN |
E32609
|
entity |
| Predicate | airportNoiseRestrictions |
P31134
|
FINISHED |
| Object | curfew-like limitations |
—
|
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: curfew-like limitations | Statement: [SAN, airportNoiseRestrictions, curfew-like limitations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airportNoiseRestrictions Context triple: [SAN, airportNoiseRestrictions, curfew-like limitations]
-
A.
hasNoiseAbatementProcedures
chosen
Indicates that specific measures or procedures are in place to reduce or control noise associated with the related entity or activity.
-
B.
flightRules
Indicates the regulatory or procedural rules that govern how a flight must be conducted or operated.
-
C.
rangeAgainstAircraft
Indicates that one entity measures, determines, or engages the distance to an aircraft target.
-
D.
appliesToAirspaceOf
Indicates that a rule, restriction, or condition is specifically relevant to, or in effect within, a particular airspace.
-
E.
hasNearbyGeneralAviationAirport
Indicates that an entity is located close to a general aviation airport, such that the airport can reasonably serve it for non-commercial or private air traffic.
- 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_69a885eba0708190999696a45cbec816 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abba712388819091d68a4bb99f6b17 |
completed | March 7, 2026, 5:41 a.m. |
| PD | Predicate disambiguation | batch_69abb7b4356881909217c42ccb8bb1ed |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:43 p.m.