Triple
T11398109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USP |
E270032
|
entity |
| Predicate | hasCampusIn |
P4623
|
FINISHED |
| Object | Santos |
E89307
|
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: Santos | Statement: [USP, hasCampusIn, Santos]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Santos Context triple: [USP, hasCampusIn, Santos]
-
A.
Santos
chosen
Santos is a major Brazilian port city on the coast of São Paulo state, known for its extensive coffee export history and popular beachfront.
-
B.
Santos
Santos is a common Portuguese surname shared by numerous notable figures in politics, sports, and the arts across Portuguese-speaking countries.
-
C.
Santonio
Santonio is a locality or district within the municipality of Piove di Sacco in the Veneto region of northern Italy.
-
D.
Gomes
Gomes is a common Portuguese surname shared by many individuals, including the artist Fernanda Gomes.
-
E.
Sampaio
Sampaio is a Portuguese surname borne by various notable figures in fields such as politics, sports, and entertainment.
- 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_69d6aacdbc6c8190af6dc3d5f5d22836 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d80019d3d48190a2f473deb6eae33a |
completed | April 9, 2026, 7:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e58cd74280819092f8c420630f4889 |
completed | April 20, 2026, 2:17 a.m. |
Created at: April 8, 2026, 9:34 p.m.