Triple
T65233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Born–Oppenheimer approximation |
E1297
|
entity |
| Predicate | approximationType |
P4447
|
FINISHED |
| Object | adiabatic approximation |
—
|
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: adiabatic approximation | Statement: [Born–Oppenheimer approximation, approximationType, adiabatic approximation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximationType Context triple: [Born–Oppenheimer approximation, approximationType, adiabatic approximation]
-
A.
adaptationType
Indicates the specific kind or category of adaptation that relates one entity to another or to a particular context.
-
B.
typeOfOperation
Indicates the specific kind or category of operation being performed or referenced in a given context.
-
C.
isAbout
Indicates that one entity has as its subject, focus, or primary concern the content, topic, or theme represented by another entity.
-
D.
settlementType
Indicates the specific kind or category of human settlement an entity represents, such as a city, village, town, or hamlet.
-
E.
usageType
Indicates the specific manner, purpose, or context in which something is used or intended to be used.
- 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_69a24ba4f760819081f6638a3c70538a |
completed | Feb. 28, 2026, 1:57 a.m. |
| NER | Named-entity recognition | batch_69a2516eda54819090f5c14384d4eab1 |
completed | Feb. 28, 2026, 2:22 a.m. |
| PD | Predicate disambiguation | batch_69a24ea5c140819080409a968c8d2ce8 |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a2516d98e88190b79261bd3fcadd9b |
completed | Feb. 28, 2026, 2:22 a.m. |
Created at: Feb. 28, 2026, 2:02 a.m.