Triple
T876459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa Justa Lift |
E18928
|
entity |
| Predicate | connects |
P390
|
FINISHED |
| Object | Baixa |
E20143
|
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: Baixa | Statement: [Santa Justa Lift, connects, Baixa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baixa Context triple: [Santa Justa Lift, connects, Baixa]
-
A.
Baixa
chosen
Baixa is Lisbon’s historic downtown district, known for its grid-planned streets, grand plazas, and Pombaline architecture rebuilt after the 1755 earthquake.
-
B.
Barra
Barra is a scenic island in the Outer Hebrides of Scotland, known for its rugged coastline, Gaelic culture, and the unique beach runway at Barra Airport.
-
C.
Barra
Barra is the surname of Mary Barra, the prominent American business executive and CEO of General Motors.
-
D.
Obarrio
Obarrio is a prominent upscale neighborhood in Panama City known for its concentration of banks, corporate offices, and modern high-rise buildings.
-
E.
Colma
Colma is a small town in San Mateo County, California, best known for its numerous cemeteries and nickname as the "City of Souls."
- 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_69a4938db1f081909bcd1ad2713b6096 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4acaf30a48190a10ed7fee464c444 |
completed | March 1, 2026, 9:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7c71e7c448190aa128bfaf26daead |
completed | March 4, 2026, 5:46 a.m. |
Created at: March 1, 2026, 7:39 p.m.