Triple
T25697516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Freisa |
E644361
|
entity |
| Predicate | notableDOCRegion |
P193985
|
FINISHED |
| Object | Freisa d’Asti DOC |
—
|
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: Freisa d’Asti DOC | Statement: [Freisa, notableDOCRegion, Freisa d’Asti DOC]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableDOCRegion Context triple: [Freisa, notableDOCRegion, Freisa d’Asti DOC]
-
A.
cinemaRegion
Indicates that a cinema is located within, or associated with, a particular geographic or administrative region.
-
B.
notableDiversityRegion
Indicates that a region is recognized for having significant diversity, such as in its population, culture, or environment.
-
C.
notableInRegion
Indicates that an entity is recognized as notable, prominent, or significant within a specified geographic region.
-
D.
notableTourRegion
Indicates that an entity is a region recognized as a significant or popular destination for tours or tourism.
-
E.
filmRegion
Indicates the geographic region or market in which a film is released, distributed, or primarily associated.
- F. None of above. chosen
Provenance (4 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_69fd5d48855c8190bd93070b6a00d8b5 |
completed | May 8, 2026, 3:49 a.m. |
| PD | Predicate disambiguation | batch_69fd5c9aabb88190912800d90184a89d |
completed | May 8, 2026, 3:46 a.m. |
| PDg | Predicate description generation | batch_69fd5d47da488190a4f2dbd44a0a83b2 |
completed | May 8, 2026, 3:49 a.m. |
Created at: April 21, 2026, 8:38 p.m.