Triple
T10986110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Atlantic division |
E259633
|
entity |
| Predicate | hasNumberOfStatesAndDistricts |
P96464
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [South Atlantic division, hasNumberOfStatesAndDistricts, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfStatesAndDistricts Context triple: [South Atlantic division, hasNumberOfStatesAndDistricts, 9]
-
A.
hasNumberOfProvinces
Indicates the total count of provinces associated with a given entity.
-
B.
hasNumberOfCounties
Indicates the relationship that specifies how many counties are associated with or contained within a given entity.
-
C.
hasStateOrDistrict
Indicates that an entity is associated with, located in, or belongs to a particular state or district.
-
D.
hasNumberOfConstituencies
Indicates the specific count of constituencies associated with an entity.
-
E.
hasTwoDistrictsInState
Indicates that an entity is associated with exactly two distinct districts within a specified 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_69d6aa895f4c8190887a15460ef622f4 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d787b2e4a88190a81504eee77e2298 |
completed | April 9, 2026, 11:04 a.m. |
| PD | Predicate disambiguation | batch_69d72e9055908190b438f039574aaaaf |
completed | April 9, 2026, 4:44 a.m. |
| PDg | Predicate description generation | batch_69d732242fdc8190be77d1f730a42935 |
completed | April 9, 2026, 4:59 a.m. |
Created at: April 8, 2026, 9:24 p.m.