Triple
T3158631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | type-I superconductors |
E66049
|
entity |
| Predicate | areModeledBy |
P28233
|
FINISHED |
| Object | BCS theory for conventional superconductors |
—
|
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: BCS theory for conventional superconductors | Statement: [type-I superconductors, areModeledBy, BCS theory for conventional superconductors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areModeledBy Context triple: [type-I superconductors, areModeledBy, BCS theory for conventional superconductors]
-
A.
hasModelledFor
Indicates that one entity has served as a model for another entity, typically in a professional or representational context such as art, photography, or fashion.
-
B.
isModelOf
chosen
Indicates that one entity serves as a representation or abstraction that captures the structure or behavior of another entity.
-
C.
possibleModel
Indicates that one entity can serve as a potential or candidate model or template for another entity.
-
D.
usedByModel
Indicates that something (such as a resource, method, or component) is utilized or consumed by a particular model.
-
E.
hasRealModel
Indicates that an abstract, theoretical, or simplified entity is associated with a corresponding concrete or physically instantiated model in the real world.
- 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_69ad85850c1481908a9e9c6242238de2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada5ed82a08190a1bdcf18ee593c79 |
completed | March 8, 2026, 4:38 p.m. |
| PD | Predicate disambiguation | batch_69ad9dfbf0348190952a6bca8fc5fed1 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:05 p.m.