Triple
T2612673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wisconsin Evangelical Lutheran Synod |
E58811
|
entity |
| Predicate | schoolCountEstimate |
P30984
|
FINISHED |
| Object | hundreds of Lutheran elementary and high schools |
—
|
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: hundreds of Lutheran elementary and high schools | Statement: [Wisconsin Evangelical Lutheran Synod, schoolCountEstimate, hundreds of Lutheran elementary and high schools]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: schoolCountEstimate Context triple: [Wisconsin Evangelical Lutheran Synod, schoolCountEstimate, hundreds of Lutheran elementary and high schools]
-
A.
servesStudentPopulation
Indicates that an entity provides services, resources, or support to a defined group of students.
-
B.
studentPopulationLevel
Indicates the relative size or magnitude of the student population associated with an entity.
-
C.
hasApproximateStudents
chosen
Indicates that an entity is associated with an estimated or approximate number of students, rather than an exact count.
-
D.
numberOfCampuses
Indicates the total count of campuses associated with a given entity.
-
E.
numberOfUniversities
Indicates the quantity of universities associated with a given entity.
- 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_69ab4ac444dc819099614e534dd6021f |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd89325308190985598373eb0d296 |
completed | March 7, 2026, 7:49 a.m. |
| PD | Predicate disambiguation | batch_69abd80cd7fc81909e9696db2919129f |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:50 p.m.