Triple
T7891186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juancho E. Yrausquin Airport |
E183236
|
entity |
| Predicate | hasApronCapacity |
P1433
|
FINISHED |
| Object | several small aircraft |
—
|
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: several small aircraft | Statement: [Juancho E. Yrausquin Airport, hasApronCapacity, several small aircraft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApronCapacity Context triple: [Juancho E. Yrausquin Airport, hasApronCapacity, several small aircraft]
-
A.
hasApron
Indicates that one entity possesses or is wearing an apron in relation to another context or entity.
-
B.
hasApronType
Indicates that an entity is associated with or characterized by a specific type or category of apron.
-
C.
hasMilitaryApron
Indicates that a location or facility includes a designated apron area specifically used for military aircraft operations.
-
D.
canHold
chosen
Indicates that one entity has the capacity or ability to contain, support, or carry another entity.
-
E.
hasGarment
Indicates that one entity possesses, wears, or is associated with a particular garment.
- 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_69ca828c474c8190a254d6499871eaff |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb39ee137081908e87e35016c3a176 |
completed | March 31, 2026, 3:05 a.m. |
| PD | Predicate disambiguation | batch_69cae92b0cd881908e715a10d3252e83 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5 p.m.