Triple
T638418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 01L/19R |
E16676
|
entity |
| Predicate | hasLightingSystem |
P13444
|
FINISHED |
| Object | runway edge lights |
—
|
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: runway edge lights | Statement: [Runway 01L/19R, hasLightingSystem, runway edge lights]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLightingSystem Context triple: [Runway 01L/19R, hasLightingSystem, runway edge lights]
-
A.
hasLighting
Indicates that one entity is equipped with, contains, or is characterized by a particular type or configuration of lighting.
-
B.
hasRunwayLighting
chosen
Indicates that a runway is equipped with lighting systems to aid visibility and operations, typically during low-light or night conditions.
-
C.
hasInteriorFeature
Indicates that an entity contains or includes a specific feature within its interior space.
-
D.
hasBaggageSystem
Indicates that an entity is equipped with or utilizes a baggage handling system.
-
E.
hasOnboardComputer
Indicates that one entity is equipped with or contains an onboard computer system.
- 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_69a4936be1c88190af56540324b57da7 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a17b125481909a6ab53424954792 |
completed | March 1, 2026, 8:28 p.m. |
| PD | Predicate disambiguation | batch_69a49d0629308190bcc137639567f7c2 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:35 p.m.