Triple
T7188743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | becquerel |
E167633
|
entity |
| Predicate | conversionFromCurie |
P75651
|
FINISHED |
| Object | 1 Ci = 3.7×10^10 Bq |
—
|
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: 1 Ci = 3.7×10^10 Bq | Statement: [becquerel, conversionFromCurie, 1 Ci = 3.7×10^10 Bq]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: conversionFromCurie Context triple: [becquerel, conversionFromCurie, 1 Ci = 3.7×10^10 Bq]
-
A.
conversionName
Indicates that one entity is the name or label assigned to a specific conversion event or conversion process associated with another entity.
-
B.
conversionUse
Indicates that one entity is used as a means, method, or context for converting another entity from one form, state, or representation to another.
-
C.
convertsTo
Indicates that one entity is transformed or changed into another entity, typically resulting in a different state, form, or representation.
-
D.
eraOfConversion
Indicates the specific historical period or era during which the conversion event or change took place.
-
E.
conversionPlace
Indicates the location where one entity is transformed, converted, or changed into another form or state.
- 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_69c6888b5248819090499a884ee3ec39 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e8e3d9188190ba2792098d76fb86 |
completed | March 27, 2026, 8:30 p.m. |
| PD | Predicate disambiguation | batch_69c6e752385c819096fbab55566ee2a8 |
completed | March 27, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69c6e8b5f6508190af28e06a7959d717 |
completed | March 27, 2026, 8:29 p.m. |
Created at: March 27, 2026, 2:50 p.m.