Triple
T15373702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brownsville South Padre Island International Airport |
E367611
|
entity |
| Predicate | runway17_35Length |
P6291
|
FINISHED |
| Object | 6000 feet |
—
|
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: 6000 feet | Statement: [Brownsville South Padre Island International Airport, runway17_35Length, 6000 feet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runway17_35Length Context triple: [Brownsville South Padre Island International Airport, runway17_35Length, 6000 feet]
-
A.
runway17_35Use
Indicates use or operational activity occurring on runway 17/35.
-
B.
runwayLength
chosen
Indicates the length of a runway associated with an airport or airfield.
-
C.
runwayWidth
Indicates the measured width of a runway as a spatial dimension.
-
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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e5d6f808190b0a4cdb35dc89e69 |
completed | April 16, 2026, 1:41 a.m. |
| PD | Predicate disambiguation | batch_69ded27742a881909cd73cc5c7d062fd |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:18 a.m.