Triple
T13419027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ribera del Duero |
E313288
|
entity |
| Predicate | notableNearbyTown |
P14082
|
FINISHED |
| Object | Peñafiel |
E369116
|
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: Peñafiel | Statement: [Ribera del Duero, notableNearbyTown, Peñafiel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peñafiel Context triple: [Ribera del Duero, notableNearbyTown, Peñafiel]
-
A.
Peñafiel
chosen
Peñafiel is a historic town in Spain renowned for its medieval castle and wine-making tradition in the Ribera del Duero region.
-
B.
Ubaque
Ubaque is a municipality in central Colombia known for its rural Andean landscapes and traditional agricultural communities.
-
C.
Cañete
Cañete is a coastal province and agricultural hub in central Peru, known for its fertile valleys, Afro-Peruvian cultural heritage, and production of crops like grapes and cotton.
-
D.
Cañete
Cañete is a historic town in the province of Cuenca, Spain, known for its medieval architecture and hilltop castle.
-
E.
Vizcaína
Vizcaína was one of the ships in Christopher Columbus’s fourth voyage to the Americas in the early 16th century.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d806ad0c44819088833ae1ec9e9690 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaeb8416c8190a00dde0917c26f51 |
completed | April 12, 2026, 2:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f73082a2548190aefc0f202b84165c |
completed | May 3, 2026, 11:24 a.m. |
Created at: April 9, 2026, 9:39 p.m.