Triple
T376948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 15L/33R |
E8391
|
entity |
| Predicate | hasMarkings |
P5950
|
FINISHED |
| Object | precision instrument runway markings |
—
|
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: precision instrument runway markings | Statement: [Runway 15L/33R, hasMarkings, precision instrument runway markings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMarkings Context triple: [Runway 15L/33R, hasMarkings, precision instrument runway markings]
-
A.
hasMarker
Indicates that one entity possesses, is associated with, or is identified by a specific marker.
-
B.
hasSignage
chosen
Indicates that appropriate signs or visual markers are present to convey information, directions, warnings, or identification related to the associated entity.
-
C.
petalMarkings
Indicates the pattern, color, or distinctive markings present on the petals of a flower in relation to the flower they belong to.
-
D.
hasTypeOfInsignia
Indicates that an entity bears or is associated with a specific kind or category of insignia.
-
E.
hasSpray
Indicates that one entity possesses, contains, or is equipped with a spray or spraying capability in relation to another entity or context.
- 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_69a2e7f2ec648190b42bc7db424f8109 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec1804108190a1e94526b71289ea |
completed | Feb. 28, 2026, 1:22 p.m. |
| PD | Predicate disambiguation | batch_69a2e96351cc8190a55adf95f8c27e9e |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.