Triple
T29310710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Knot (1947) |
E743224
|
entity |
| Predicate | creatorTrainedAt |
P194177
|
FINISHED |
| Object | Bauhaus |
—
|
NE NERFINISHED |
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: Bauhaus | Statement: [Knot (1947), creatorTrainedAt, Bauhaus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: creatorTrainedAt Context triple: [Knot (1947), creatorTrainedAt, Bauhaus]
-
A.
isTrainedBy
Indicates that one entity receives training, instruction, or coaching from another entity.
-
B.
trainerModel
Indicates that one entity serves as the trainer or training source for a model entity.
-
C.
pretrainedOn
Indicates that a model has been trained in advance using a specified dataset or data source before being applied to downstream tasks.
-
D.
trainedAs
Indicates that one entity has received education or instruction to perform the role, profession, or function represented by another entity.
-
E.
trainedNear
Indicates that one entity received training at a location that is geographically close to another specified entity or location.
- 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_69f0912502c8819087d9e8398ee991a8 |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69fd67191cf88190b53ecbf5be3564e9 |
completed | May 8, 2026, 4:31 a.m. |
| PD | Predicate disambiguation | batch_69fd654fdaac81908e67e75194710f06 |
completed | May 8, 2026, 4:23 a.m. |
| PDg | Predicate description generation | batch_69fd67182c348190aa84a02e08dbf4e1 |
completed | May 8, 2026, 4:31 a.m. |
Created at: April 28, 2026, 1:16 p.m.