Triple
T14272533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orange Airport |
E353825
|
entity |
| Predicate | runway11_29Use |
P113524
|
FINISHED |
| Object | instrument and visual operations |
—
|
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: instrument and visual operations | Statement: [Orange Airport, runway11_29Use, instrument and visual operations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runway11_29Use Context triple: [Orange Airport, runway11_29Use, instrument and visual operations]
-
A.
runway
Indicates a relationship where a runway serves as the takeoff and landing surface used by aircraft at an airport or airfield.
-
B.
runway12_30Usage
Indicates the use or operational status of runway 12/30 for aircraft movements or related airport activities.
-
C.
runway17_35Use
Indicates use or operational activity occurring on runway 17/35.
-
D.
usesRunwayOf
Indicates that one entity makes use of the runway that belongs to or is associated with another entity.
-
E.
runwayPair
Indicates that two runways are associated or grouped together as a functional pair, typically for coordinated or complementary use.
- F. None of above. chosen
Provenance (4 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_69d8278d25148190abf1a8c8f5f533ad |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de65811d7c8190b075909a6570d415 |
completed | April 14, 2026, 4:04 p.m. |
| PD | Predicate disambiguation | batch_69de2a7d586c8190846ff242bbf5ac53 |
completed | April 14, 2026, 11:52 a.m. |
| PDg | Predicate description generation | batch_69de2e07d1f88190bdcd20967e484718 |
completed | April 14, 2026, 12:07 p.m. |
Created at: April 10, 2026, 1:10 a.m.