Triple
T15727190
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helene Fischer |
E381247
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Krasnoyarsk |
E199248
|
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: Krasnoyarsk | Statement: [Helene Fischer, placeOfBirth, Krasnoyarsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Krasnoyarsk Context triple: [Helene Fischer, placeOfBirth, Krasnoyarsk]
-
A.
Krasnoyarsk
chosen
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.
-
B.
Irkutsk
Irkutsk is a major city in southeastern Siberia, Russia, historically significant as a political and administrative center and a key hub during the Russian Civil War.
-
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.
Novokuznetsk
Novokuznetsk is a major industrial city in southwestern Siberia, Russia, known for its large metallurgical and coal-mining industries.
-
E.
Tyumen
Tyumen is a historic city in western Siberia, Russia, known as an early Russian settlement in Siberia and now a major industrial and administrative center.
- 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04fb4cc0081909efe330339474017 |
completed | April 16, 2026, 2:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff28d38a4819085bf051b566c17e7 |
completed | May 10, 2026, 2:50 a.m. |
Created at: April 10, 2026, 4:46 a.m.