Triple
T10425285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pavel Datsyuk |
E245769
|
entity |
| Predicate | playedFor |
P2170
|
FINISHED |
| Object | Ak Bars Kazan |
E156583
|
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: Ak Bars Kazan | Statement: [Pavel Datsyuk, playedFor, Ak Bars Kazan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ak Bars Kazan Context triple: [Pavel Datsyuk, playedFor, Ak Bars Kazan]
-
A.
Ak Bars Kazan
chosen
Ak Bars Kazan is a professional ice hockey club from Kazan, Russia, renowned as one of the country’s most successful and decorated teams.
-
B.
Kazanin
Kazanin is a Russian-language surname most notably borne by comedian and television personality Stepan Kazanin.
-
C.
Buynaksk
Buynaksk is a significant urban center in the Republic of Dagestan in southern Russia, known for its strategic location in the North Caucasus region.
-
D.
Kazan
Kazan is a major city in western Russia and the capital of the Republic of Tatarstan, known for its rich Tatar-Russian cultural heritage and historic Kremlin.
-
E.
Kazanh
Kazanh is a locality within Turkey’s Ankara Province, situated in the Central Anatolia region.
- 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_69d381bf3dc08190bf35a2643e4e8f22 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea48cb348190ad4432f263300592 |
completed | April 7, 2026, 11:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d7fc2160208190b6384190d9537df4 |
completed | April 9, 2026, 7:21 p.m. |
Created at: April 6, 2026, 12:12 p.m.