Triple
T9937937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Møller scattering |
E194002
|
entity |
| Predicate | hasCrossSectionDependence |
P77528
|
FINISHED |
| Object | fine-structure constant squared |
—
|
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: fine-structure constant squared | Statement: [Møller scattering, hasCrossSectionDependence, fine-structure constant squared]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCrossSectionDependence Context triple: [Møller scattering, hasCrossSectionDependence, fine-structure constant squared]
-
A.
crossSectionDependsOn
chosen
Indicates that the value or behavior of a cross section is determined or influenced by another quantity, condition, or parameter.
-
B.
hasCrossSection
Indicates that one entity represents or possesses the cross-sectional shape, profile, or slice of another entity.
-
C.
crossesSectionOf
Indicates that one entity passes through or over a specific segment or portion of another entity.
-
D.
interactionCrossSection
Indicates the effective likelihood or probability that a specified interaction or reaction will occur between entities (such as particles) under given conditions.
-
E.
hasCross
Indicates that one entity possesses, displays, or is marked by a cross in relation to another entity or context.
- 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_69ca82e409348190a393777356b80a2a |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb5e64760819094f599f158d32f33 |
completed | April 2, 2026, 12:18 a.m. |
| PD | Predicate disambiguation | batch_69cd1d9428cc81909b4b4938566d78a7 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:44 p.m.