Triple
T27450469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 23 |
E692423
|
entity |
| Predicate | hasPhraseologyExample |
P132994
|
FINISHED |
| Object | “Cleared to land runway two three” |
—
|
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: “Cleared to land runway two three” | Statement: [Runway 23, hasPhraseologyExample, “Cleared to land runway two three”]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPhraseologyExample Context triple: [Runway 23, hasPhraseologyExample, “Cleared to land runway two three”]
-
A.
hasRhymingSlangExample
Indicates that one entity serves as an example of rhyming slang associated with another entity.
-
B.
isUsedInPhrase
Indicates that something (such as a word, expression, or symbol) appears as a component within a particular phrase.
-
C.
hasExampleWordPronunciation
Indicates that an entity is associated with a specific example of how a word is pronounced.
-
D.
typicalPhrase
chosen
Indicates that the object is a phrase commonly or characteristically used in connection with the subject.
-
E.
usedPhrase
Indicates that one entity employed or expressed a particular phrase in speech, writing, or another form of communication.
- 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_69ef5206c9248190b5975c2a7f9d229c |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f68805b4848190b75da14996d52a38 |
completed | May 2, 2026, 11:25 p.m. |
| PD | Predicate disambiguation | batch_69f68609c0b08190a8e1238a4d97c270 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 27, 2026, 12:47 p.m.