Triple
T769116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 10L/28R |
E16240
|
entity |
| Predicate | usedExtensivelyBy |
P11801
|
FINISHED |
| Object | commercial airliners |
—
|
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: commercial airliners | Statement: [Runway 10L/28R, usedExtensivelyBy, commercial airliners]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedExtensivelyBy Context triple: [Runway 10L/28R, usedExtensivelyBy, commercial airliners]
-
A.
widelyUsedIn
chosen
Indicates that something is commonly or extensively utilized within a particular context, domain, or group.
-
B.
usedWith
Indicates that one entity is typically or appropriately employed together with another entity in a combined or complementary use.
-
C.
usedFor
Indicates that one entity serves a purpose, function, or role in accomplishing, enabling, or supporting another entity or activity.
-
D.
usedPrimarilyIn
Indicates that something is mainly or most commonly employed within a particular context, domain, or purpose.
-
E.
usedOn
Indicates that one entity is applied to, operated on, or otherwise utilized in relation to another entity.
- 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_69a49369a0848190af883934cee3db4c |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a70376988190be2826259f5281ab |
completed | March 1, 2026, 8:52 p.m. |
| PD | Predicate disambiguation | batch_69a4a508c42c8190850a0ac7844a3ea9 |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.