Triple
T1807347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | variational autoencoders |
E40250
|
entity |
| Predicate | objectiveIncludes |
P32133
|
FINISHED |
| Object | reconstruction loss |
—
|
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: reconstruction loss | Statement: [variational autoencoders, objectiveIncludes, reconstruction loss]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: objectiveIncludes Context triple: [variational autoencoders, objectiveIncludes, reconstruction loss]
-
A.
includes
Indicates that one entity contains, encompasses, or has another entity as a part, member, or subset.
-
B.
reasoningIncludes
Indicates that a reasoning process or argument explicitly incorporates or makes use of the referenced element as one of its components or steps.
-
C.
includedWith
Indicates that one entity is provided or packaged together as part of another entity.
-
D.
includesAbstract
Indicates that one entity contains or incorporates the abstract or summary section of another entity.
-
E.
secondaryGoal
Indicates that something serves as a subordinate or supporting objective in addition to a primary goal.
- 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_69a88643a3388190a612f2ebe1fb29e7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab694d75ac8190a4d61399c04b9fb9 |
completed | March 6, 2026, 11:54 p.m. |
| PD | Predicate disambiguation | batch_69aa61d6b8ec8190a1597b2e44ea6534 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab694bf6a08190a02ce2fc979e6701 |
completed | March 6, 2026, 11:54 p.m. |
Created at: March 4, 2026, 7:32 p.m.