Triple
T38514296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Albariño |
E921998
|
entity |
| Predicate | notablePortugueseRegion |
P204702
|
FINISHED |
| Object | Vinho Verde |
E202054
|
NE 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: Vinho Verde | Statement: [Albariño, notablePortugueseRegion, Vinho Verde]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notablePortugueseRegion Context triple: [Albariño, notablePortugueseRegion, Vinho Verde]
-
A.
locatedInCentralPortugal
Indicates that the subject is situated within the central region of Portugal.
-
B.
populationRankInPortugal
Indicates the relative position of an entity in terms of population size compared to other entities within Portugal.
-
C.
countryNamePortuguese
Indicates the Portuguese-language name assigned to a given country.
-
D.
roleInPortugal
Indicates that an entity holds or has held a specific role, position, or function within the context of Portugal.
-
E.
aliadoPrincipalDePortugal
Indicates that one entity serves as the primary ally of Portugal in a political, military, or strategic context.
- F. None of above. chosen
Provenance (5 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_69f76ea3c5448190aa7002fc1ba3f874 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41d663cc6c8190ae8420f67b0f9760 |
completed | June 29, 2026, 2:20 a.m. |
| PD | Predicate disambiguation | batch_6a037a1e32108190897356d6a7fed879 |
completed | May 12, 2026, 7:06 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:32 p.m.