Triple
T7353566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | county of New Jersey |
E169564
|
entity |
| Predicate | numberOfCountiesInState |
P27148
|
FINISHED |
| Object | 21 |
—
|
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: 21 | Statement: [county of New Jersey, numberOfCountiesInState, 21]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCountiesInState Context triple: [county of New Jersey, numberOfCountiesInState, 21]
-
A.
hasNumberOfCounties
chosen
Indicates the relationship that specifies how many counties are associated with or contained within a given entity.
-
B.
numberPerCounty
Indicates the quantity or count of something associated with each individual county.
-
C.
numberOfStates
Indicates the total count of distinct states or conditions associated with an entity or system.
-
D.
countyNumberInStateFormation
Indicates the ordinal position a county held among all counties created within a particular state at the time of that state's formation.
-
E.
eachCountyHas
Indicates that for every county in a given set or context, there exists at least one associated item, attribute, or entity satisfying a specified condition.
- 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_69c68a5878888190968ce4d04db8d69f |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f139505c8190a7158cf59a6e089e |
completed | March 27, 2026, 9:06 p.m. |
| PD | Predicate disambiguation | batch_69c6f02aeeb8819099d1626566cec18b |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:05 p.m.