Triple
T8650773
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Danbury Municipal Airport |
E205093
|
entity |
| Predicate | runway17/35Orientation |
P6272
|
FINISHED |
| Object | 170/350 degrees |
—
|
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: 170/350 degrees | Statement: [Danbury Municipal Airport, runway17/35Orientation, 170/350 degrees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runway17/35Orientation Context triple: [Danbury Municipal Airport, runway17/35Orientation, 170/350 degrees]
-
A.
hasRunwayOrientation
chosen
Indicates that a runway is aligned or oriented in a specific directional heading.
-
B.
runway
Indicates a relationship where a runway serves as the takeoff and landing surface used by aircraft at an airport or airfield.
-
C.
orientationType
Indicates the specific kind or category of orientation relationship that exists between entities (such as spatial, directional, or alignment-based orientation).
-
D.
runwayPair
Indicates that two runways are associated or grouped together as a functional pair, typically for coordinated or complementary use.
-
E.
legOrientation
Indicates the relative positioning or directional alignment of an entity’s leg(s) with respect to a reference frame or another object.
- 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_69ca834e56848190abb0eeaec9dedd32 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc48150e6c8190a7a3b92b4b640858 |
completed | March 31, 2026, 10:17 p.m. |
| PD | Predicate disambiguation | batch_69cc45619460819091e83ffdec99c865 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:29 p.m.