Triple
T30506689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TJBQ |
E776291
|
entity |
| Predicate | hasLongestRunwayLength |
P6291
|
FINISHED |
| Object | approximately 11800 ft |
—
|
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: approximately 11800 ft | Statement: [TJBQ, hasLongestRunwayLength, approximately 11800 ft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLongestRunwayLength Context triple: [TJBQ, hasLongestRunwayLength, approximately 11800 ft]
-
A.
hasLongestRunwayIn
Indicates that an entity possesses the runway of greatest length within a specified location or region.
-
B.
runwayLength
chosen
Indicates the length of a runway associated with an airport or airfield.
-
C.
isPrimaryRunwayOf
Indicates that a runway serves as the main or principal runway for a particular airport or airfield.
-
D.
hasRunwayLengthCategory
Indicates that an airport or airfield is associated with a specific categorical range of runway lengths (e.g., short, medium, long).
-
E.
runwayLengthUnit
Indicates the unit of measurement used to express the length of a runway in the relationship.
- 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_69f2249a155c8190b1d512106007e9bb |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fd32848ea88190a71e6df402bbb30e |
completed | May 8, 2026, 12:47 a.m. |
| PD | Predicate disambiguation | batch_69fd2d7e95588190991d5f21e25155df |
completed | May 8, 2026, 12:25 a.m. |
Created at: April 29, 2026, 8:15 p.m.