Triple
T4191547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KORD |
E89047
|
entity |
| Predicate | usesRunway |
P1654
|
FINISHED |
| Object | Runway 04R/22L |
E92200
|
NE 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: Runway 04R/22L | Statement: [KORD, usesRunway, Runway 04R/22L]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Runway 04R/22L Context triple: [KORD, usesRunway, Runway 04R/22L]
-
A.
Runway 04R/22L
Runway 04R/22L is one of the main parallel runways at Nice Côte d’Azur Airport, serving commercial air traffic on the French Riviera.
-
B.
Runway 04L/22R
Runway 04L/22R is one of the main parallel runways at Nice Côte d’Azur Airport, serving commercial air traffic on the French Riviera.
-
C.
Runway 04L/22R
Runway 04L/22R is one of the primary paved runways used for aircraft takeoffs and landings at Wuhan Tianhe International Airport in Wuhan, China.
-
D.
Runway 04L/22R
chosen
Runway 04L/22R is one of the primary parallel runways at Chicago O'Hare International Airport, used for handling high volumes of commercial air traffic.
-
E.
Runway 4R/22L
Runway 4R/22L is one of the primary paved runways at Boston Logan International Airport, used for handling a significant share of the airport’s commercial air traffic.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69aed9569a4481908b6c1fcec2a11e21 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af034169f88190a8eb170ba6008b79 |
completed | March 9, 2026, 5:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bfc60426248190947e1c58fe4dd1b1 |
completed | March 22, 2026, 10:35 a.m. |
Created at: March 9, 2026, 3:46 p.m.