Triple
T36704293
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Efficient Estimation of Word Representations in Vector Space |
E906311
|
entity |
| Predicate | trainingSpeed |
P186232
|
FINISHED |
| Object | significantly faster than previous neural language models |
—
|
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: significantly faster than previous neural language models | Statement: [Efficient Estimation of Word Representations in Vector Space, trainingSpeed, significantly faster than previous neural language models]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingSpeed Context triple: [Efficient Estimation of Word Representations in Vector Space, trainingSpeed, significantly faster than previous neural language models]
-
A.
trainingStability
Indicates the degree to which a training process proceeds consistently without large fluctuations, divergence, or instability in its behavior or outcomes.
-
B.
trainingPeriod
Indicates the duration or phase during which training or instruction is formally conducted.
-
C.
trainingSurface
Indicates the surface or environment on which a training activity or practice takes place.
-
D.
trainingMethod
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
-
E.
trainingCompute
Indicates the amount or configuration of computational resources used to train a model or system.
- F. None of above. chosen
Provenance (4 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_69f76e7195c48190b5580c9cfb01e95f |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f7cabacc1481909e839454ce1057f7 |
completed | May 3, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69f7c8999a348190abc1895eaa6e036d |
completed | May 3, 2026, 10:13 p.m. |
| PDg | Predicate description generation | batch_69f7c9f4c7c48190ba918d8d5dc8dfd9 |
completed | May 3, 2026, 10:19 p.m. |
Created at: May 3, 2026, 4:12 p.m.