Triple
T19193004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jorge Sampaio |
E469890
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Sampaio |
—
|
NE NERFINISHED |
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: Sampaio | Statement: [Jorge Sampaio, familyName, Sampaio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sampaio Context triple: [Jorge Sampaio, familyName, Sampaio]
-
A.
Sampaio
chosen
Sampaio is a Portuguese surname borne by various notable figures in fields such as politics, sports, and entertainment.
-
B.
Werdenberg
Werdenberg is a region in the Swiss canton of St. Gallen, known for its historic town and castle near the Rhine Valley.
-
C.
Teixeira
Teixeira is a Portuguese-origin surname borne by numerous individuals, including the American former Major League Baseball first baseman Mark Teixeira.
-
D.
Ferraz de Vasconcelos
Ferraz de Vasconcelos is a municipality in the metropolitan region of São Paulo, Brazil, known for its urban character and integration into the Greater São Paulo area.
-
E.
Gonçalves
Gonçalves is a common Portuguese surname, especially prevalent in Portugal and Brazil, derived from the given name Gonçalo.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dd0ad9088190a173b32657ae2e7a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f8a3434881908acc4063a9ee9386 |
completed | April 20, 2026, 9:57 a.m. |
Created at: April 10, 2026, 12:07 p.m.