Triple
T1318608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allred–Rochow electronegativity scale |
E28163
|
entity |
| Predicate | usesQuantity |
P26961
|
FINISHED |
| Object | effective nuclear charge at covalent radius |
—
|
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: effective nuclear charge at covalent radius | Statement: [Allred–Rochow electronegativity scale, usesQuantity, effective nuclear charge at covalent radius]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesQuantity Context triple: [Allred–Rochow electronegativity scale, usesQuantity, effective nuclear charge at covalent radius]
-
A.
quantityType
Indicates that one entity is the type or category of quantity to which another entity (a specific measured or measurable amount) belongs.
-
B.
quantifies
Indicates that one entity expresses or specifies the amount, number, or degree of another entity.
-
C.
quantificationType
Indicates the specific kind or category of quantity or measurement being applied in a given context.
-
D.
numberOfUnits
Indicates the quantity or count of discrete units associated with an entity or relationship.
-
E.
isLocalQuantity
Indicates that a quantity is specific to a particular context, location, or subsystem rather than being globally applicable.
- 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_69a498532c3481909223b74af2e578df |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c1780be8819083a9365b8a49305d |
completed | March 1, 2026, 10:45 p.m. |
| PD | Predicate disambiguation | batch_69a4beebcb348190964bd7215811942c |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4bfc2134c81909cbaaa151d96e9a8 |
completed | March 1, 2026, 10:37 p.m. |
Created at: March 1, 2026, 7:55 p.m.