Triple
T9407799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alcazaba |
E226629
|
entity |
| Predicate | accessFrom |
P1985
|
FINISHED |
| Object | Capileira |
E238939
|
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: Capileira | Statement: [Alcazaba, accessFrom, Capileira]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Capileira Context triple: [Alcazaba, accessFrom, Capileira]
-
A.
Capileira
chosen
Capileira is a picturesque mountain village in Spain’s Alpujarras region, known for its traditional whitewashed houses and dramatic location on the southern slopes of the Sierra Nevada.
-
B.
Cabeceiras de Basto
Cabeceiras de Basto is a small municipality in northern Portugal known for its rural landscapes, traditional Minho architecture, and cultural heritage.
-
C.
Pedreira
Pedreira is a municipality in the state of São Paulo, Brazil, known for its ceramics industry and decorative household goods.
-
D.
Trancoso
Trancoso is a historic Portuguese town in the Centro Region, known for its medieval walls, castle, and well-preserved old quarter.
-
E.
Cabaceiras
Cabaceiras is a historic town in the Brazilian state of Paraíba, known for its well-preserved colonial architecture and frequent use as a filming location for movies and television.
- 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_69ca843280488190bc65600e843ef9e6 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd5252b3fc8190b0808a10987728c8 |
completed | April 1, 2026, 5:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1102414e8819097a1bb58a3ded630 |
completed | April 4, 2026, 1:20 p.m. |
Created at: March 30, 2026, 7:47 p.m.