Triple
T6058371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yelizovo Airport |
E134969
|
entity |
| Predicate | ICAOcode |
P419
|
FINISHED |
| Object | UHPP |
E561205
|
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: UHPP | Statement: [Yelizovo Airport, ICAOcode, UHPP]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UHPP Context triple: [Yelizovo Airport, ICAOcode, UHPP]
-
A.
UHPP
chosen
UHPP is the ICAO airport code for Petropavlovsk-Kamchatsky Airport, a major air gateway on Russia’s Kamchatka Peninsula.
-
B.
UPP
UPP is a reporting mark used by the Union Pacific Railroad to identify certain passenger cars and related rolling stock in its fleet.
-
C.
UHWW
UHWW is the ICAO airport code assigned to Vladivostok International Airport in Russia.
-
D.
UHHH
UHHH is the ICAO airport code for Khabarovsk Novy Airport in Khabarovsk, Russia.
-
E.
HUP
HUP is a major academic medical center in Philadelphia that serves as the flagship teaching hospital of the University of Pennsylvania's health system.
- 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_69c00877b6d4819096b0e163728b73a3 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0570e64408190ae7a2504f63bb58a |
completed | March 22, 2026, 8:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11d0e06288190b4389b43825d5929 |
completed | March 23, 2026, 10:59 a.m. |
Created at: March 22, 2026, 4:10 p.m.