Triple
T10417388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | McDonnell Douglas MD-95 |
E245553
|
entity |
| Predicate | aircraftNoiseStandard |
P44487
|
FINISHED |
| Object | Stage 3 compliant |
—
|
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: Stage 3 compliant | Statement: [McDonnell Douglas MD-95, aircraftNoiseStandard, Stage 3 compliant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aircraftNoiseStandard Context triple: [McDonnell Douglas MD-95, aircraftNoiseStandard, Stage 3 compliant]
-
A.
aircraftNoiseCategory
Indicates the classification of an aircraft based on the level or type of noise it produces.
-
B.
hasNoiseAbatementProcedures
Indicates that specific measures or procedures are in place to reduce or control noise associated with the related entity or activity.
-
C.
noiseCompliance
chosen
Indicates that an entity adheres to specified rules or standards governing acceptable noise levels or sound emissions.
-
D.
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.
-
E.
rangeAgainstAircraft
Indicates that one entity measures, determines, or engages the distance to an aircraft target.
- 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_69d381be340c8190b05998703d42d224 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea1194e08190a18c3b3002147493 |
completed | April 7, 2026, 11:27 a.m. |
| PD | Predicate disambiguation | batch_69d4dfb9d3648190aaabed901f22a8c0 |
completed | April 7, 2026, 10:43 a.m. |
Created at: April 6, 2026, 12:11 p.m.