Triple
T36490063
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dzmitry Bahdanau |
E899028
|
entity |
| Predicate | citationsCountRange |
P97311
|
FINISHED |
| Object | >10000 citations for attention NMT paper |
—
|
LITERAL 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: >10000 citations for attention NMT paper | Statement: [Dzmitry Bahdanau, citationsCountRange, >10000 citations for attention NMT paper]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: citationsCountRange Context triple: [Dzmitry Bahdanau, citationsCountRange, >10000 citations for attention NMT paper]
-
A.
hasCitationCount
chosen
Indicates that an entity (such as a publication) is associated with a specific number representing how many times it has been cited.
-
B.
oftenCitedBy
Indicates that one entity (such as a work or source) is frequently referenced or cited by another entity.
-
C.
hasCitationImpact
Indicates that one entity (such as a publication, author, or venue) exerts measurable influence on scholarly work through citations it receives or generates.
-
D.
citationNumber
Indicates the specific numeric identifier assigned to a citation within a document or reference list.
-
E.
citationUsage
Indicates how one work cites, references, or otherwise makes use of another work as a source.
- F. None of above.
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_69f76e5ad4588190bdbce60c52fbb785 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c371931c8190afb1d4dd5157f92c |
completed | May 3, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69f7c1b91fd88190ab85afd626603769 |
completed | May 3, 2026, 9:44 p.m. |
Created at: May 3, 2026, 4:10 p.m.