Triple
T25697511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Freisa |
E644361
|
entity |
| Predicate | traditionalRegionUse |
P116633
|
FINISHED |
| Object | Monferrato |
—
|
NE NERFINISHED |
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: Monferrato | Statement: [Freisa, traditionalRegionUse, Monferrato]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: traditionalRegionUse Context triple: [Freisa, traditionalRegionUse, Monferrato]
-
A.
traditionalUseRegion
chosen
Indicates the geographic region where something has been traditionally used or practiced.
-
B.
usedInRegion
Indicates that something is utilized or applied within a specific geographic or administrative region.
-
C.
modernUsageRegion
Indicates the geographic region where something is currently or most commonly used in modern times.
-
D.
hasTypicalUsageRegion
Indicates that something is most commonly or characteristically used within a particular geographic region.
-
E.
traditionalTerritoryUse
Indicates that an entity uses or occupies a territory in ways that follow long-established, customary, or ancestral practices.
- 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_69e77e82c9bc8190893090b2f6c64f1d |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f6a8df16a88190a23820e64a3b1f92 |
completed | May 3, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69f6a751d5e48190a77dcecbe7ef9f0b |
completed | May 3, 2026, 1:39 a.m. |
Created at: April 21, 2026, 8:38 p.m.