Triple
T36491712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Relation Networks for few-shot learning |
E899065
|
entity |
| Predicate | usesLoss |
P31982
|
FINISHED |
| Object | mean squared error on relation scores |
—
|
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: mean squared error on relation scores | Statement: [Relation Networks for few-shot learning, usesLoss, mean squared error on relation scores]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesLoss Context triple: [Relation Networks for few-shot learning, usesLoss, mean squared error on relation scores]
-
A.
causedLossOf
Indicates that one entity brought about or was responsible for another entity experiencing a loss.
-
B.
usesPointsForLoss
Indicates that a system or rule assigns or deducts points to represent or account for a loss.
-
C.
usesLossFunction
chosen
Indicates that one entity employs a particular loss function as part of its optimization or learning process.
-
D.
lossType
Indicates the specific category or nature of a loss associated with an entity or event.
-
E.
losses
Indicates that an entity experiences a decrease in value, quantity, or advantage as a result of some event or comparison.
- 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.