Triple
T11177461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | São Rafael |
E264447
|
entity |
| Predicate | sisterShip |
P3142
|
FINISHED |
| Object | Bérrio |
E263217
|
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: Bérrio | Statement: [São Rafael, sisterShip, Bérrio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bérrio Context triple: [São Rafael, sisterShip, Bérrio]
-
A.
Bérrio
chosen
Bérrio was a Portuguese carrack that served as one of the ships in Vasco da Gama’s pioneering fleet on the first voyage from Portugal to India.
-
B.
Bicesse
Bicesse is a town in Angola known primarily as the site where the Bicesse Accords peace agreement was signed.
-
C.
Raposeira
Raposeira is a small village in Portugal’s Algarve region, known for its rural charm and proximity to the Atlantic coast near Vila do Bispo.
-
D.
Celorico da Beira
Celorico da Beira is a historic municipality in central Portugal, known for its medieval castle and strong tradition of Serra da Estrela cheese production.
-
E.
Coruripe
Coruripe is a coastal municipality in northeastern Brazil known for its beaches, fishing activities, and sugarcane agriculture.
- 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_69d6aa9dafac8190bd90d2c74f661aa7 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8987e1081909b28a0bdb866beae |
completed | April 9, 2026, 5:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4838f19388190af6fde7d4275ce2a |
completed | April 19, 2026, 7:26 a.m. |
Created at: April 8, 2026, 9:29 p.m.