Triple
T37251130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 16L/34R |
E923990
|
entity |
| Predicate | hasRunwayNumber16Side |
P54806
|
FINISHED |
| Object | 16L |
—
|
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: 16L | Statement: [Runway 16L/34R, hasRunwayNumber16Side, 16L]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRunwayNumber16Side Context triple: [Runway 16L/34R, hasRunwayNumber16Side, 16L]
-
A.
hasRunwaySide
Indicates that a runway is located on or associated with a particular side or boundary of another feature (such as an airport or airfield area).
-
B.
hasRunwayNumber
Indicates that an airport or airfield runway is assigned a specific identifying number.
-
C.
hasRunwayDesignationSide
chosen
Indicates that a runway designation is associated with a specific side or direction of the runway (e.g., left, right, or center).
-
D.
hasRunwayNumberRange
Indicates that an entity (such as an airport or airfield) has runways whose identification numbers fall within a specified numeric range.
-
E.
hasRunwayType
Indicates that an airport or airfield has a runway of a specified type or surface classification.
- 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_69f76eaabb4c819093b751b139dad551 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fb4134225081909fd60703b8cae397 |
completed | May 6, 2026, 1:25 p.m. |
| PD | Predicate disambiguation | batch_69fb35bf767081908de8345358ca7f44 |
completed | May 6, 2026, 12:36 p.m. |
Created at: May 3, 2026, 4:15 p.m.