Triple
T13527345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | complementarity principle |
E323045
|
entity |
| Predicate | teachingExample |
P110688
|
FINISHED |
| Object | electron diffraction through a crystal |
—
|
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: electron diffraction through a crystal | Statement: [complementarity principle, teachingExample, electron diffraction through a crystal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teachingExample Context triple: [complementarity principle, teachingExample, electron diffraction through a crystal]
-
A.
coreTeaching
Indicates that an entity serves as a primary or foundational teaching or instructional activity for another entity.
-
B.
teachingFunction
Indicates that one entity serves in a teaching role or performs instructional duties toward others.
-
C.
taughtThat
Indicates that one entity provided instruction or education to another entity about a specific subject, skill, or concept.
-
D.
teachableSource
Indicates that one entity serves as a source from which another can be taught or can learn.
-
E.
teachableFrom
Indicates that one entity can be taught or learned from another entity, capturing a directional teachability or learnability relationship between them.
- 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_69d80766a21881909f21a1b7421d3b8a |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafb8e0cc8190b47f6aeb8ced470e |
completed | April 12, 2026, 2:44 p.m. |
| PD | Predicate disambiguation | batch_69dbae1046c48190b4ee98c6c9cb9d85 |
completed | April 12, 2026, 2:37 p.m. |
| PDg | Predicate description generation | batch_69dbaecc98cc8190829f5be759c4f1e3 |
completed | April 12, 2026, 2:40 p.m. |
Created at: April 9, 2026, 9:44 p.m.