Triple
T12717624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Florence County, South Carolina |
E303889
|
entity |
| Predicate | countyNumberInState |
P106323
|
FINISHED |
| Object | 41 |
—
|
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: 41 | Statement: [Florence County, South Carolina, countyNumberInState, 41]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countyNumberInState Context triple: [Florence County, South Carolina, countyNumberInState, 41]
-
A.
countyNumberInStateFormation
Indicates the ordinal position a county held among all counties created within a particular state at the time of that state's formation.
-
B.
numberPerCounty
Indicates the quantity or count of something associated with each individual county.
-
C.
hasNumberOfCounties
Indicates the relationship that specifies how many counties are associated with or contained within a given entity.
-
D.
provinceNumber
Indicates a relationship where an entity is assigned a specific numerical identifier corresponding to a province.
-
E.
countyNumberInColorado
Indicates that a given county is assigned a specific official county number within the state of Colorado.
- 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_69d7bdf084148190ab9d513dc0735af4 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9620bd6148190a2f50067a4c18c14 |
completed | April 10, 2026, 8:48 p.m. |
| PD | Predicate disambiguation | batch_69d960c088dc8190b0e63312c54e4c6c |
completed | April 10, 2026, 8:42 p.m. |
| PDg | Predicate description generation | batch_69d961acadb8819098de743bc951fedb |
completed | April 10, 2026, 8:46 p.m. |
Created at: April 9, 2026, 5:23 p.m.