Triple
T180116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teniente Rodolfo Marsh Martin Aerodrome |
E3853
|
entity |
| Predicate | hasRunwayOrientation |
P6272
|
FINISHED |
| Object | 11/29 |
—
|
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: 11/29 | Statement: [Teniente Rodolfo Marsh Martin Aerodrome, hasRunwayOrientation, 11/29]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRunwayOrientation Context triple: [Teniente Rodolfo Marsh Martin Aerodrome, hasRunwayOrientation, 11/29]
-
A.
runway
Indicates a relationship where a runway serves as the takeoff and landing surface used by aircraft at an airport or airfield.
-
B.
runwaySurface
Indicates the type or condition of the surface material that a runway is made of or covered with.
-
C.
numberOfRunways
Indicates the quantity of runways associated with a given entity, such as an airport or airfield.
-
D.
containsAirfield
Indicates that a location or area includes at least one airfield within its boundaries.
-
E.
landingGearType
Indicates the specific kind or configuration of landing gear that an object (typically an aircraft or vehicle) uses.
- F. None of above. chosen
Provenance (4 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_69a25497e2f08190a040f8c6e1842643 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25901a9188190b8f510bec8c8e7f2 |
completed | Feb. 28, 2026, 2:54 a.m. |
| PD | Predicate disambiguation | batch_69a2566ccc288190add5624ede96d82b |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a2575e7c7c819095167d8a862c255a |
completed | Feb. 28, 2026, 2:47 a.m. |
Created at: Feb. 28, 2026, 2:40 a.m.