Triple
T14269819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KWI |
E353749
|
entity |
| Predicate | hasRunway |
P105
|
FINISHED |
| Object |
Runway 15R/33L
Runway 15R/33L is a primary paved runway at Kuwait International Airport used for handling commercial air traffic.
|
E1116602
|
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 15R/33L | Statement: [KWI, hasRunway, Runway 15R/33L]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Runway 15R/33L Context triple: [KWI, hasRunway, Runway 15R/33L]
-
A.
Runway 15R/33L
Runway 15R/33L is one of the primary parallel runways at Boston Logan International Airport, used for both arrivals and departures.
-
B.
Runway 15R/33L
Runway 15R/33L is a primary paved runway at Long Island MacArthur Airport used for commercial and general aviation takeoffs and landings.
-
C.
Runway 15R/33L
Runway 15R/33L is a primary paved runway at Baghdad International Airport used for commercial and military aircraft operations.
-
D.
Runway 15R/33L
Runway 15R/33L is a major runway at King Khalid International Airport in Riyadh, Saudi Arabia, used for handling large commercial aircraft operations.
-
E.
Runway 15L/33R
Runway 15L/33R is one of the primary paved runways at Baltimore/Washington International Thurgood Marshall Airport, used for handling commercial air traffic in both directions.
- 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 15R/33L Triple: [KWI, hasRunway, Runway 15R/33L]
Generated description
Runway 15R/33L is a primary paved runway at Kuwait International Airport used for handling commercial air traffic.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Runway 15R/33L Target entity description: Runway 15R/33L is a primary paved runway at Kuwait International Airport used for handling commercial air traffic.
-
A.
Runway 15R/33L
Runway 15R/33L is one of the primary parallel runways at Boston Logan International Airport, used for both arrivals and departures.
-
B.
Runway 15R/33L
Runway 15R/33L is a primary paved runway at Long Island MacArthur Airport used for commercial and general aviation takeoffs and landings.
-
C.
Runway 15R/33L
Runway 15R/33L is a primary paved runway at Baghdad International Airport used for commercial and military aircraft operations.
-
D.
Runway 15R/33L
Runway 15R/33L is a major runway at King Khalid International Airport in Riyadh, Saudi Arabia, used for handling large commercial aircraft operations.
-
E.
Runway 15L/33R
Runway 15L/33R is one of the primary paved runways at Baltimore/Washington International Thurgood Marshall Airport, used for handling commercial air traffic in both directions.
- 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_69d8278d25148190abf1a8c8f5f533ad |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de657fe6708190b41de48c43cff647 |
completed | April 14, 2026, 4:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdfb73334c8190a96a92d199c3b101 |
completed | May 8, 2026, 3:04 p.m. |
| NEDg | Description generation | batch_69fdfd9389848190a9f5c29aa94f0c0e |
completed | May 8, 2026, 3:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fdfe2af2248190a0d0096d8039a591 |
completed | May 8, 2026, 3:15 p.m. |
Created at: April 10, 2026, 1:10 a.m.