Triple
T1095675
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TOTal Elastic and diffractive cross section Measurement |
E24265
|
entity |
| Predicate | measuresQuantity |
P4227
|
FINISHED |
| Object | total cross section |
—
|
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: total cross section | Statement: [TOTal Elastic and diffractive cross section Measurement, measuresQuantity, total cross section]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: measuresQuantity Context triple: [TOTal Elastic and diffractive cross section Measurement, measuresQuantity, total cross section]
-
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.
unitOfMeasure
Indicates that one entity specifies the standard unit in which the quantity or value of another entity is measured.
-
C.
hasMeasurement
Indicates that an entity is associated with a specific measured value, often including a unit or measurement context.
-
D.
quantifies
chosen
Indicates that one entity expresses or specifies the amount, number, or degree of another entity.
-
E.
quantificationType
Indicates the specific kind or category of quantity or measurement being applied in a given 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_69a4940542308190ac2a0b1f730b7cfc |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b99e92308190b8a8c499e1630672 |
completed | March 1, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69a4b743175481908f3967e589717c55 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.