Triple
T11003545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pointer Networks |
E260057
|
entity |
| Predicate | publishedAtConference |
P21395
|
FINISHED |
| Object | NIPS 2015 |
E96742
|
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: NIPS 2015 | Statement: [Pointer Networks, publishedAtConference, NIPS 2015]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: NIPS 2015 Context triple: [Pointer Networks, publishedAtConference, NIPS 2015]
-
A.
NIPS
NIPS is the acronym for the Northern Ireland Prison Service, the government agency responsible for managing prisons and overseeing the custody and rehabilitation of offenders in Northern Ireland.
-
B.
NeurIPS
chosen
NeurIPS is a premier international conference focused on advances in machine learning, artificial intelligence, and computational neuroscience.
-
C.
ICML
ICML (International Conference on Machine Learning) is one of the premier global academic conferences focused on research in machine learning and related fields.
-
D.
ICLR
ICLR (International Conference on Learning Representations) is a leading annual machine learning conference focused on deep learning and representation learning research.
-
E.
“A fast learning algorithm for deep belief nets”
“A fast learning algorithm for deep belief nets” is a seminal 2006 paper by Geoffrey Hinton that introduced an efficient unsupervised pretraining method for deep neural networks using stacked restricted Boltzmann machines.
- 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.