Triple
T3984561
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pico Ruivo |
E86839
|
entity |
| Predicate | connectedByTrailTo |
P40019
|
FINISHED |
| Object | Achada do Teixeira |
E404858
|
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: Achada do Teixeira | Statement: [Pico Ruivo, connectedByTrailTo, Achada do Teixeira]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Achada do Teixeira Context triple: [Pico Ruivo, connectedByTrailTo, Achada do Teixeira]
-
A.
Achada do Teixeira
chosen
Achada do Teixeira is a popular mountain plateau and viewpoint in Madeira, Portugal, serving as a main access point for hikes into the island’s central highlands.
-
B.
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.
-
C.
Pedreira
Pedreira is a municipality in the state of São Paulo, Brazil, known for its ceramics industry and decorative household goods.
-
D.
Pilão Cão
Pilão Cão is a small rural village located on the island of Maio in Cape Verde.
-
E.
Poço da Panela
Poço da Panela is a historic and traditionally residential neighborhood located along the Capibaribe River in the city of Recife, Brazil.
- 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_69aed93fd9d4819085d3b2137d2346cb |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af0197a0a0819085d746f51c7fc51b |
completed | March 9, 2026, 5:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b54c4a769c819097fb9bfd5890a4f9 |
completed | March 14, 2026, 11:53 a.m. |
Created at: March 9, 2026, 3:33 p.m.