Triple
T26573692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wisconsin Circuit Courts |
E666892
|
entity |
| Predicate | numberOfCountiesServed |
P27148
|
FINISHED |
| Object | 72 |
—
|
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: 72 | Statement: [Wisconsin Circuit Courts, numberOfCountiesServed, 72]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCountiesServed Context triple: [Wisconsin Circuit Courts, numberOfCountiesServed, 72]
-
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.
inhabitantsServed
Indicates the relationship in which a service, facility, or resource provides for or meets the needs of a specified group of inhabitants.
-
D.
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.
-
E.
hasAdjacentMunicipalitiesServed
Indicates that a municipality has neighboring municipalities that are also served by the same service, system, or administrative arrangement.
- 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_69ee9cfa21c081909e4e36e087debfc6 |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69fdf5d05cc481909ec9e1b1f0784279 |
completed | May 8, 2026, 2:40 p.m. |
| PD | Predicate disambiguation | batch_69fdf0cdd6948190838864ab3120dfa6 |
completed | May 8, 2026, 2:18 p.m. |
Created at: April 27, 2026, 1:59 a.m.