Triple
T16871715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sergey Dvortsevoy |
E421182
|
entity |
| Predicate | notableAwardForWork |
P41228
|
FINISHED |
| Object | Tulpan |
E1237654
|
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: Tulpan | Statement: [Sergey Dvortsevoy, notableAwardForWork, Tulpan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tulpan Context triple: [Sergey Dvortsevoy, notableAwardForWork, Tulpan]
-
A.
Tulpan
chosen
Tulpan is a 2008 Kazakh drama film by Sergey Dvortsevoy that portrays the harsh yet humorous life of a young man seeking to become a shepherd on the remote Kazakh steppe.
-
B.
Krokus
Krokus is a Swiss hard rock and heavy metal band best known for its energetic 1980s releases and international hits like "Heatstrokes" and "Screaming in the Night."
-
C.
Tulipa
Tulipa is a genus of bulbous flowering plants best known for its colorful tulip blooms, widely cultivated as ornamental garden and cut flowers.
-
D.
Bostan
Bostan is a town in Pakistan’s Balochistan province that serves as a regional railway junction and transit point between Quetta and Chaman.
-
E.
Yesenin
Yesenin is a renowned Russian lyric poet known for his evocative depictions of rural life and his tragic, short-lived career in the early 20th century.
- 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_69d889d470fc8190b4aec199636c0c56 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3b7f31b448190a21e3e4d1a0d2f73 |
completed | April 18, 2026, 4:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00c7a27f548190963c35f40f4420f8 |
completed | May 10, 2026, 6 p.m. |
Created at: April 10, 2026, 5:29 a.m.