Triple
T10704882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waterloo Regional Airport |
E252377
|
entity |
| Predicate | hasICAOCode |
P419
|
FINISHED |
| Object | KALO |
E252377
|
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: KALO | Statement: [Waterloo Regional Airport, hasICAOCode, KALO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KALO Context triple: [Waterloo Regional Airport, hasICAOCode, KALO]
-
A.
KALO
chosen
KALO is the ICAO airport code for Waterloo Regional Airport in Waterloo, Iowa, United States.
-
B.
KAL
KAL is the ICAO airline designator used to identify Korean Air in international aviation operations.
-
C.
KLAL
KLAL is the ICAO airport code for Lakeland Linder International Airport in Lakeland, Florida, a regional airport known for general aviation and cargo operations.
-
D.
Kaul
Kaul is a Kashmiri Pandit surname historically associated with prominent Indian families, including that of Kamala Nehru.
-
E.
Kiesen
Kiesen is a municipality in the canton of Bern, Switzerland, served by a station on the Bern–Thun railway line.
- 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_69d6aa5cbabc8190973e683950d89faf |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fddeb060819094cd125a68070eb2 |
completed | April 9, 2026, 1:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d998fe56dc8190ae0c987b28ec6206 |
completed | April 11, 2026, 12:42 a.m. |
Created at: April 8, 2026, 9:12 p.m.