Triple
T3049380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coperni |
E83529
|
entity |
| Predicate | runwayFormat |
P45341
|
FINISHED |
| Object | conceptual runway shows |
—
|
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: conceptual runway shows | Statement: [Coperni, runwayFormat, conceptual runway shows]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runwayFormat Context triple: [Coperni, runwayFormat, conceptual runway shows]
-
A.
runway
Indicates a relationship where a runway serves as the takeoff and landing surface used by aircraft at an airport or airfield.
-
B.
hasRunwayNumber
Indicates that an airport or airfield runway is assigned a specific identifying number.
-
C.
runwayPerformance
Indicates the performance characteristics or behavior of an entity (such as an aircraft or vehicle) when operating on a runway, including factors like acceleration, deceleration, and required distances.
-
D.
runwayWidth
Indicates the measured width of a runway as a spatial dimension.
-
E.
runwayLength
Indicates the length of a runway associated with an airport or airfield.
- 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_69ad8b24924c8190a9bb6f61d519e4ae |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9bb00a348190877c9cbf0f13cba4 |
completed | March 8, 2026, 3:54 p.m. |
| PD | Predicate disambiguation | batch_69ad962195388190856013a2519c2b0f |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad9b272a408190ba0ce09bdeea76c9 |
completed | March 8, 2026, 3:52 p.m. |
Created at: March 8, 2026, 3:01 p.m.