Triple
T18724015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Attention Is All You Need |
E457850
|
entity |
| Predicate | benchmarkDataset |
P16906
|
FINISHED |
| Object | WMT 2014 English-to-German translation |
—
|
NE NERFINISHED |
How this triple was built (3 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: WMT 2014 English-to-German translation | Statement: [Attention Is All You Need, benchmarkDataset, WMT 2014 English-to-German translation]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WMT 2014 English-to-German translation Context triple: [Attention Is All You Need, benchmarkDataset, WMT 2014 English-to-German translation]
-
A.
WMT English-French dataset
The WMT English-French dataset is a large-scale parallel corpus of English–French sentence pairs widely used as a benchmark for training and evaluating machine translation systems.
-
B.
WMT
WMT is the stock ticker symbol for Walmart Inc., the multinational retail corporation that operates a chain of hypermarkets, discount department stores, and grocery stores.
-
C.
Google Neural Machine Translation system
The Google Neural Machine Translation system is Google's deep learning–based framework that provides high-quality, end-to-end neural machine translation across many languages in Google Translate and related services.
-
D.
DeepL Translator
DeepL Translator is an AI-powered neural machine translation service known for its high-quality translations and support for multiple languages across web and desktop platforms.
-
E.
Deutsches Referenzkorpus
Deutsches Referenzkorpus is a large, curated corpus of contemporary German language used as a key empirical resource for linguistic research and lexicography.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: WMT 2014 English-to-German translation Target entity description: WMT 2014 English-to-German translation is a widely used machine translation benchmark dataset for evaluating neural translation models between English and German.
-
A.
WMT English-French dataset
The WMT English-French dataset is a large-scale parallel corpus of English–French sentence pairs widely used as a benchmark for training and evaluating machine translation systems.
-
B.
WMT
WMT is the stock ticker symbol for Walmart Inc., the multinational retail corporation that operates a chain of hypermarkets, discount department stores, and grocery stores.
-
C.
Google Neural Machine Translation system
The Google Neural Machine Translation system is Google's deep learning–based framework that provides high-quality, end-to-end neural machine translation across many languages in Google Translate and related services.
-
D.
DeepL Translator
DeepL Translator is an AI-powered neural machine translation service known for its high-quality translations and support for multiple languages across web and desktop platforms.
-
E.
Deutsches Referenzkorpus
Deutsches Referenzkorpus is a large, curated corpus of contemporary German language used as a key empirical resource for linguistic research and lexicography.
- F. None of above. chosen
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_69d8d393ba9c8190a8b03b04ddbb0a09 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e56abcfc048190a01dee959e768768 |
completed | April 19, 2026, 11:52 p.m. |
Created at: April 10, 2026, 11:50 a.m.