Triple
T17943363
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yakutia Airlines |
E448639
|
entity |
| Predicate | focusCity |
P164
|
FINISHED |
| Object | Novosibirsk |
—
|
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: Novosibirsk | Statement: [Yakutia Airlines, focusCity, Novosibirsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Novosibirsk Context triple: [Yakutia Airlines, focusCity, Novosibirsk]
-
A.
Novosibirsk
chosen
Novosibirsk is a major city in southwestern Siberia and the third-largest city in Russia, known as an important industrial, scientific, and cultural center.
-
B.
Sibir Novosibirsk
Sibir Novosibirsk is a professional ice hockey club from Novosibirsk, Russia, that competes in the Kontinental Hockey League (KHL).
-
C.
Omsk
Omsk is one of the largest cities in southwestern Siberia, Russia, serving as a major industrial, cultural, and transportation hub on the Irtysh River.
-
D.
Tomsk
Tomsk is a historic university and research city in southwestern Siberia, known as one of the region’s oldest and most important cultural and educational centers.
-
E.
Krasnoyarsk
Krasnoyarsk is a large industrial and cultural city in central Russia, situated on the Yenisei River and known as one of the key urban centers of Siberia.
- 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_69d8b9f8cca8819099836916c56b7c95 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4ad97611c8190a861467ae51f6c48 |
completed | April 19, 2026, 10:25 a.m. |
Created at: April 10, 2026, 10:21 a.m.