Triple
T11003315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation |
E260052
|
entity |
| Predicate | publishedIn |
P309
|
FINISHED |
| Object | EMNLP 2014 |
E736213
|
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: EMNLP 2014 | Statement: [Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, publishedIn, EMNLP 2014]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: EMNLP 2014 Context triple: [Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, publishedIn, EMNLP 2014]
-
A.
EMNLP
chosen
EMNLP (Empirical Methods in Natural Language Processing) is a leading annual conference in computational linguistics and natural language processing research.
-
B.
NAACL 2018
NAACL 2018 was a major computational linguistics conference where the influential ELMo deep contextualized word representation model was introduced.
-
C.
ACM Transactions on Asian and Low-Resource Language Information Processing
ACM Transactions on Asian and Low-Resource Language Information Processing is a peer-reviewed scholarly journal focusing on computational linguistics, natural language processing, and information processing for Asian and other low-resource languages.
-
D.
NeurIPS
NeurIPS is a premier international conference focused on advances in machine learning, artificial intelligence, and computational neuroscience.
-
E.
“A Computer Program for Understanding Natural Language”
“A Computer Program for Understanding Natural Language” is a landmark 1968 paper by Terry Winograd that presents an early natural language understanding system capable of interpreting and executing commands in a simulated blocks world.
- 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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797546f448190946ee6442d657dc5 |
completed | April 9, 2026, 12:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3453d181081908cb58a957f4d1295 |
completed | April 18, 2026, 8:47 a.m. |
Created at: April 8, 2026, 9:25 p.m.