Triple
T28602828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New Jersey congressional districts |
E723959
|
entity |
| Predicate | numberOfDistrictsAfter2010Census |
P1679
|
FINISHED |
| Object | 12 |
—
|
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: 12 | Statement: [New Jersey congressional districts, numberOfDistrictsAfter2010Census, 12]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfDistrictsAfter2010Census Context triple: [New Jersey congressional districts, numberOfDistrictsAfter2010Census, 12]
-
A.
numberOfDistricts
chosen
Indicates the total count of districts associated with a given entity or area.
-
B.
numberOfSenateDistricts
Indicates the total count of senate districts associated with a given entity or jurisdiction.
-
C.
eachDistrictElects
Indicates that every electoral district selects or chooses its own representative or set of representatives.
-
D.
dataUsedForApportionment
Indicates that certain data is utilized as the basis for determining how something (such as representation, resources, or allocations) is apportioned among different units or groups.
-
E.
numberOfDistrictMembers
Indicates the relationship that specifies how many members are associated with a given district.
- 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_69f01d80b1908190980594837604b8c7 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69ff795d25d08190b7584c72be39d309 |
completed | May 9, 2026, 6:13 p.m. |
| PD | Predicate disambiguation | batch_69ff78a90fbc8190a62c57456dc1d4ad |
completed | May 9, 2026, 6:10 p.m. |
Created at: April 28, 2026, 4:25 a.m.