Triple
T1265457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ripon, Wisconsin |
E12589
|
entity |
| Predicate | localEconomySector |
P71
|
FINISHED |
| Object | education |
—
|
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: education | Statement: [Ripon, Wisconsin, localEconomySector, education]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: localEconomySector Context triple: [Ripon, Wisconsin, localEconomySector, education]
-
A.
economicSectorSourceOfWealth
Indicates that a particular economic sector is the primary source from which an entity derives its wealth or income.
-
B.
hasIndustrialSector
Indicates that an entity is associated with, operates in, or belongs to a particular industrial sector or branch of economic activity.
-
C.
economicSectorIssue
Indicates that there is a problem, challenge, or concern affecting a particular economic sector.
-
D.
sector
chosen
Indicates that an entity operates in, belongs to, or is associated with a particular economic or industrial sector.
-
E.
economicImpactRegion
Indicates the region or geographic area that experiences or is affected by a particular economic impact.
- 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_69a4933352e08190ac617291985e76c0 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4c036deb881909b234894347c75c6 |
completed | March 1, 2026, 10:39 p.m. |
| PD | Predicate disambiguation | batch_69a4bb7058e88190825b0cba5ee60b51 |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:50 p.m.