Triple
T8540863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NBO |
E202189
|
entity |
| Predicate | hasRunway |
P105
|
FINISHED |
| Object |
Runway 24
Runway 24 is one of the primary landing and takeoff runways at Jomo Kenyatta International Airport in Nairobi, Kenya.
|
E776877
|
NE FINISHED |
How this triple was built (4 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: Runway 24 | Statement: [NBO, hasRunway, Runway 24]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Runway 24 Context triple: [NBO, hasRunway, Runway 24]
-
A.
Runway 21
Runway 21 is one of the primary landing and takeoff runways serving Charles B. Wheeler Downtown Airport in Kansas City, Missouri.
-
B.
Runway 19
Runway 19 is one of the primary landing and takeoff runways serving Charles B. Wheeler Downtown Airport in Kansas City, Missouri.
-
C.
Runway 22
Runway 22 is the runway end aligned approximately with a 220-degree magnetic heading, commonly used for aircraft takeoffs and landings in that direction.
-
D.
Runway 5
Runway 5 is an airport runway designation indicating an approximate magnetic heading of 50 degrees, typically used when runway numbers are updated to reflect shifts in Earth’s magnetic variation.
-
E.
Runway 34
Runway 34 is one end of an airport runway aligned approximately toward the 340-degree magnetic heading, typically used for aircraft takeoffs and landings in that direction.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Runway 24 Triple: [NBO, hasRunway, Runway 24]
Generated description
Runway 24 is one of the primary landing and takeoff runways at Jomo Kenyatta International Airport in Nairobi, Kenya.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Runway 24 Target entity description: Runway 24 is one of the primary landing and takeoff runways at Jomo Kenyatta International Airport in Nairobi, Kenya.
-
A.
Runway 21
Runway 21 is one of the primary landing and takeoff runways serving Charles B. Wheeler Downtown Airport in Kansas City, Missouri.
-
B.
Runway 19
Runway 19 is one of the primary landing and takeoff runways serving Charles B. Wheeler Downtown Airport in Kansas City, Missouri.
-
C.
Runway 22
Runway 22 is the runway end aligned approximately with a 220-degree magnetic heading, commonly used for aircraft takeoffs and landings in that direction.
-
D.
Runway 5
Runway 5 is an airport runway designation indicating an approximate magnetic heading of 50 degrees, typically used when runway numbers are updated to reflect shifts in Earth’s magnetic variation.
-
E.
Runway 34
Runway 34 is one end of an airport runway aligned approximately toward the 340-degree magnetic heading, typically used for aircraft takeoffs and landings in that direction.
- F. None of above. chosen
Provenance (5 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_69ca832461e88190a654c5e44e233aa8 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe6e10bc081909a7210c577b807fb |
completed | March 31, 2026, 3:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d016c8b6f0819084d14fec00aad335 |
completed | April 3, 2026, 7:36 p.m. |
| NEDg | Description generation | batch_69d019059e8481909a696575366aa0b6 |
completed | April 3, 2026, 7:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d019a2736c8190880c8f3786cf353b |
completed | April 3, 2026, 7:48 p.m. |
Created at: March 30, 2026, 6:18 p.m.