Triple
T21351600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 4R/22L |
E526494
|
entity |
| Predicate | icaoAirportCode |
P419
|
FINISHED |
| Object | KORD |
—
|
NE NERFINISHED |
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: KORD | Statement: [Runway 4R/22L, icaoAirportCode, KORD]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KORD Context triple: [Runway 4R/22L, icaoAirportCode, KORD]
-
A.
KORD
chosen
KORD is the ICAO airport code for Chicago O'Hare International Airport, one of the busiest and most significant air transport hubs in the United States.
-
B.
KORL
KORL is the ICAO airport code for Orlando Executive Airport, a public airport serving the Orlando, Florida area.
-
C.
Korgen
Korgen is a village in Nordland county, Norway, known as the main local hub for services and administration in the municipality of Hemnes.
-
D.
KUST
KUST is a public university in Kohat, Pakistan, known for its programs and research in science, technology, and related disciplines.
-
E.
KUST
KUST is a Chinese university in Kunming specializing in science, engineering, and technology education and research.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b51cd5cc81909ac1187971e8a8ad |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69ee5bab98148190aa14d52fd37bc894 |
completed | April 26, 2026, 6:38 p.m. |
Created at: April 16, 2026, 5:04 p.m.