Triple
T28392867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New Jersey legislative redistricting |
E719201
|
entity |
| Predicate | mapAdoptionMethod |
P112915
|
FINISHED |
| Object | majority vote of the Apportionment Commission |
—
|
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: majority vote of the Apportionment Commission | Statement: [New Jersey legislative redistricting, mapAdoptionMethod, majority vote of the Apportionment Commission]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mapAdoptionMethod Context triple: [New Jersey legislative redistricting, mapAdoptionMethod, majority vote of the Apportionment Commission]
-
A.
adoptionType
chosen
Indicates the specific category or manner in which an adoption relationship is established or recognized between entities.
-
B.
canAdopt
Indicates that one entity has the ability or permission to adopt another entity.
-
C.
adoptionLocation
Indicates the place or setting where an adoption event occurs or is formally recorded.
-
D.
adoptionFrequency
Indicates how often an entity adopts or takes on another entity, such as a practice, item, or individual, over a given period.
-
E.
dataAdozione
Indicates the date on which an adoption takes place or is officially recorded between the related entities.
- 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_69eff6ef211081909d31d9be5f5567e6 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f64cee303081908e27fadd6ef248b1 |
completed | May 2, 2026, 7:13 p.m. |
| PD | Predicate disambiguation | batch_69f641e2f1708190b45b48d6a43c51d2 |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 28, 2026, 1:14 a.m.