Triple
T24045444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brown hydroboration |
E595505
|
entity |
| Predicate | regioselectivity |
P154650
|
FINISHED |
| Object | anti-Markovnikov |
—
|
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: anti-Markovnikov | Statement: [Brown hydroboration, regioselectivity, anti-Markovnikov]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regioselectivity Context triple: [Brown hydroboration, regioselectivity, anti-Markovnikov]
-
A.
hasStereoselectivity
Indicates that a reaction or process preferentially forms or involves one stereoisomer over others.
-
B.
stereochemicalOutcome
Indicates the specific three-dimensional spatial arrangement of atoms or groups in the product(s) that results from a chemical reaction or transformation.
-
C.
regionalOrientation
Indicates how something is directed, aligned, or focused toward a particular geographic region or area.
-
D.
selectionRegion
Indicates a relationship where a specific region or area is designated as the active selection within a larger space or context.
-
E.
regionalCharacteristic
Indicates that a particular feature, quality, or attribute is typical of, or distinctive to, a specific geographic region.
- 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_69e288c06a908190899cad4531f32c9a |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d9c9391c819095ea6232fa872d5c |
completed | April 29, 2026, 10:13 a.m. |
| PD | Predicate disambiguation | batch_69f1764345388190a3102b62ddb729b4 |
completed | April 29, 2026, 3:08 a.m. |
| PDg | Predicate description generation | batch_69f1785afe3c81909be28986ffe944bf |
completed | April 29, 2026, 3:17 a.m. |
Created at: April 17, 2026, 10:13 p.m.