Triple
T18215097
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thomson scattering |
E436131
|
entity |
| Predicate | hasTotalCrossSectionValue |
P38062
|
FINISHED |
| Object | σ_T ≈ 6.65×10⁻²⁹ m² |
—
|
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: σ_T ≈ 6.65×10⁻²⁹ m² | Statement: [Thomson scattering, hasTotalCrossSectionValue, σ_T ≈ 6.65×10⁻²⁹ m²]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTotalCrossSectionValue Context triple: [Thomson scattering, hasTotalCrossSectionValue, σ_T ≈ 6.65×10⁻²⁹ m²]
-
A.
hasCrossSection
Indicates that one entity represents or possesses the cross-sectional shape, profile, or slice of another entity.
-
B.
interactionCrossSection
chosen
Indicates the effective likelihood or probability that a specified interaction or reaction will occur between entities (such as particles) under given conditions.
-
C.
crossSectionDependsOn
Indicates that the value or behavior of a cross section is determined or influenced by another quantity, condition, or parameter.
-
D.
hasCross
Indicates that one entity possesses, displays, or is marked by a cross in relation to another entity or context.
-
E.
hasAreaTotal
Indicates the total surface area associated with an entity, typically measured over its entire extent.
- 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e476a6548190bda03190c5f531ad |
completed | April 19, 2026, 2:19 p.m. |
| PD | Predicate disambiguation | batch_69e4332155d88190b106d0dceb4554af |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:32 a.m.